Decouple tracking from detection; size-relative motion; UI tuning

Engine:
- Replace centroid SimpleTracker (tracking.py) with GreedyIoUTracker in
detector.py. Detection now always runs via predict(); IDs are assigned in a
separate IoU pass, so unmatched detections are kept (full recall) instead of
being dropped by a confirm-before-emit tracker.
- Dedup keeps the oldest track_id in an overlap group so identity no longer
flickers with per-frame confidence.
- Moving condition: foot-point + object-size-relative, smoothed half-window
displacement (min_displacement_ratio) replacing frame-% box-center
displacement, rejecting box jitter on stationary objects.
- server.py enables tracking only when a rule actually uses moving.

UI:
- Sample-interval slider default 0.25s, 0.25s steps.
- Drop "dog" from default/example classes; rebuild dist bundle.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

DEMO.md

@@ -58,10 +58,11 @@ Recommended starting detector settings:
 
 Rules automatically add their referenced labels to the detector class list.
 Rules can use presence, count, near, far, moving, enter, exit, change, and
-cooldown. Moving uses lightweight same-label tracking across sampled frames;
-it requires at least three tracked observations and tolerates one missed sampled
-frame by default. Speed, direction, long-gap re-identification, and trajectory
-paths are not supported yet.
+cooldown. Moving uses detector-provided track IDs across sampled frames; YOLOE
+uses Ultralytics ByteTrack only when active rules include motion. It requires at
+least three tracked observations and tolerates one missed sampled frame by
+default. Speed, direction, long-gap re-identification, and trajectory paths are
+not supported yet.
 
 ## Prompts To Try
 

README.md

@@ -88,11 +88,13 @@ rules:
 
 Initial video conditions include presence, count, near, far, and moving. Near/far
 use the minimum horizontal/vertical gap between detection boxes in normalized
-frame percent. Moving uses lightweight same-label centroid tracking across
-sampled frames and tolerates one missed sampled frame by default to avoid repeat
-alerts from detector flicker. Gates include enabled state and cooldown. Triggers
-can fire while a condition is true, when it becomes true, when it becomes false,
-or on either change. Tiny Trigger does not yet support speed, direction, long-gap
+frame percent. Moving uses detector-provided track IDs across sampled frames;
+YOLOE enables Ultralytics ByteTrack only when active rules include a moving
+condition, so presence/near/count rules keep the higher-recall prediction path.
+Moving tolerates one missed sampled frame by default to avoid repeat alerts from
+detector flicker. Gates include enabled state and cooldown. Triggers can fire
+while a condition is true, when it becomes true, when it becomes false, or on
+either change. Tiny Trigger does not yet support speed, direction, long-gap
 re-identification, or trajectory path rules.
 
 ```yaml

frontend/dist/assets/index-DbWTYGKW.css

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20%,transparent)}}.hover\:bg-accent:hover{background-color:var(--accent)}.hover\:bg-destructive\/25:hover{background-color:var(--destructive)}@supports (color:color-mix(in lab,red,red)){.hover\:bg-destructive\/25:hover{background-color:color-mix(in oklab,var(--destructive) 25%,transparent)}}.hover\:bg-white\/5:hover{background-color:#ffffff0d}@supports (color:color-mix(in lab,red,red)){.hover\:bg-white\/5:hover{background-color:color-mix(in oklab,var(--color-white) 5%,transparent)}}.hover\:bg-white\/\[0\.02\]:hover{background-color:#ffffff05}@supports (color:color-mix(in lab,red,red)){.hover\:bg-white\/\[0\.02\]:hover{background-color:color-mix(in oklab,var(--color-white) 2%,transparent)}}.hover\:bg-white\/\[0\.025\]:hover{background-color:#ffffff06}@supports (color:color-mix(in lab,red,red)){.hover\:bg-white\/\[0\.025\]:hover{background-color:color-mix(in oklab,var(--color-white) 2.5%,transparent)}}.hover\:text-foreground:hover{color:var(--foreground)}.hover\:brightness-110:hover{--tw-brightness:brightness(110%);filter:var(--tw-blur,) var(--tw-brightness,) var(--tw-contrast,) var(--tw-grayscale,) var(--tw-hue-rotate,) var(--tw-invert,) var(--tw-saturate,) var(--tw-sepia,) var(--tw-drop-shadow,)}}.focus-visible\:border-primary\/50:focus-visible{border-color:var(--primary)}@supports (color:color-mix(in lab,red,red)){.focus-visible\:border-primary\/50:focus-visible{border-color:color-mix(in oklab,var(--primary) 50%,transparent)}}.focus-visible\:ring-2:focus-visible{--tw-ring-shadow:var(--tw-ring-inset,) 0 0 0 calc(2px + var(--tw-ring-offset-width)) var(--tw-ring-color,currentcolor);box-shadow:var(--tw-inset-shadow),var(--tw-inset-ring-shadow),var(--tw-ring-offset-shadow),var(--tw-ring-shadow),var(--tw-shadow)}.focus-visible\:ring-ring:focus-visible{--tw-ring-color:var(--ring)}.focus-visible\:ring-offset-2:focus-visible{--tw-ring-offset-width:2px;--tw-ring-offset-shadow:var(--tw-ring-inset,) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color)}.focus-visible\:outline-none:focus-visible{--tw-outline-style:none;outline-style:none}.active\:scale-\[0\.98\]:active{scale:.98}.disabled\:pointer-events-none:disabled{pointer-events:none}.disabled\:cursor-not-allowed:disabled{cursor:not-allowed}.disabled\:opacity-40:disabled{opacity:.4}.disabled\:opacity-50:disabled{opacity:.5}.data-\[state\=active\]\:bg-secondary[data-state=active]{background-color:var(--secondary)}.data-\[state\=active\]\:text-foreground[data-state=active]{color:var(--foreground)}.data-\[state\=active\]\:shadow-sm[data-state=active]{--tw-shadow:0 1px 3px 0 var(--tw-shadow-color,#0000001a), 0 1px 2px -1px var(--tw-shadow-color,#0000001a);box-shadow:var(--tw-inset-shadow),var(--tw-inset-ring-shadow),var(--tw-ring-offset-shadow),var(--tw-ring-shadow),var(--tw-shadow)}.data-\[state\=closed\]\:animate-out[data-state=closed]{animation:exit var(--tw-animation-duration,var(--tw-duration,.15s))var(--tw-ease,ease)var(--tw-animation-delay,0s)var(--tw-animation-iteration-count,1)var(--tw-animation-direction,normal)var(--tw-animation-fill-mode,none)}.data-\[state\=closed\]\:fade-out-0[data-state=closed]{--tw-exit-opacity:0}@media(min-width:40rem){.sm\:grid-cols-4{grid-template-columns:repeat(4,minmax(0,1fr))}.sm\:flex-row{flex-direction:row}.sm\:items-center{align-items:center}.sm\:justify-between{justify-content:space-between}.sm\:px-6{padding-inline:calc(var(--spacing) * 6)}}@media(min-width:48rem){.md\:flex{display:flex}}@media(min-width:64rem){.lg\:col-span-4{grid-column:span 4/span 4}.lg\:col-span-5{grid-column:span 5/span 5}.lg\:col-span-6{grid-column:span 6/span 6}.lg\:col-span-7{grid-column:span 7/span 7}.lg\:col-span-8{grid-column:span 8/span 8}.lg\:col-span-12{grid-column:span 12/span 12}.lg\:grid-cols-12{grid-template-columns:repeat(12,minmax(0,1fr))}}.\[\&_svg\]\:size-4 svg{width:calc(var(--spacing) * 4);height:calc(var(--spacing) * 4)}.\[\&_svg\]\:shrink-0 svg{flex-shrink:0}.font-display{font-family:var(--font-display)}.text-balance{text-wrap:balance}.signal-dot{box-shadow:0 0 0 0 var(--color-primary);animation:1.8s cubic-bezier(.4,0,.2,1) infinite signal-pulse}@keyframes signal-pulse{0%{box-shadow:0 0 #c5ec4580}70%{box-shadow:0 0 0 8px #c5ec4500}to{box-shadow:0 0 #c5ec4500}}.reveal{opacity:0;animation:.55s cubic-bezier(.22,1,.36,1) forwards reveal-up}@keyframes reveal-up{0%{opacity:0;transform:translateY(12px)}to{opacity:1;transform:translateY(0)}}.hairline{background-image:linear-gradient(to right,transparent,var(--color-border),transparent)}}@property --tw-animation-delay{syntax:"*";inherits:false;initial-value:0s}@property --tw-animation-direction{syntax:"*";inherits:false;initial-value:normal}@property --tw-animation-duration{syntax:"*";inherits:false}@property --tw-animation-fill-mode{syntax:"*";inherits:false;initial-value:none}@property --tw-animation-iteration-count{syntax:"*";inherits:false;initial-value:1}@property --tw-enter-blur{syntax:"*";inherits:false;initial-value:0}@property --tw-enter-opacity{syntax:"*";inherits:false;initial-value:1}@property --tw-enter-rotate{syntax:"*";inherits:false;initial-value:0}@property --tw-enter-scale{syntax:"*";inherits:false;initial-value:1}@property --tw-enter-translate-x{syntax:"*";inherits:false;initial-value:0}@property --tw-enter-translate-y{syntax:"*";inherits:false;initial-value:0}@property --tw-exit-blur{syntax:"*";inherits:false;initial-value:0}@property --tw-exit-opacity{syntax:"*";inherits:false;initial-value:1}@property --tw-exit-rotate{syntax:"*";inherits:false;initial-value:0}@property --tw-exit-scale{syntax:"*";inherits:false;initial-value:1}@property --tw-exit-translate-x{syntax:"*";inherits:false;initial-value:0}@property --tw-exit-translate-y{syntax:"*";inherits:false;initial-value:0}:root{--radius:.625rem;--background:oklch(14.5% .006 264);--foreground:oklch(93% .004 264);--card:oklch(18.5% .007 264);--card-foreground:oklch(93% .004 264);--popover:oklch(20.5% .008 264);--popover-foreground:oklch(93% .004 264);--primary:oklch(88.5% .19 122);--primary-foreground:oklch(18% .03 130);--secondary:oklch(23.5% .008 264);--secondary-foreground:oklch(93% .004 264);--muted:oklch(23.5% .008 264);--muted-foreground:oklch(64% .012 264);--accent:oklch(23.5% .008 264);--accent-foreground:oklch(93% .004 264);--destructive:oklch(63.7% .21 25.3);--destructive-foreground:oklch(97% .01 20);--warning:oklch(79% .15 78);--warning-foreground:oklch(20% .03 78);--info:oklch(78% .12 222);--info-foreground:oklch(18% .03 240);--border:oklch(100% 0 0/.08);--input:oklch(100% 0 0/.12);--ring:oklch(88.5% .19 122/.55);--font-display:"Clash Display", ui-sans-serif, system-ui, sans-serif;--font-sans:"Hanken Grotesk", ui-sans-serif, system-ui, sans-serif;--font-mono:"JetBrains Mono", ui-monospace, monospace}@property --tw-translate-x{syntax:"*";inherits:false;initial-value:0}@property --tw-translate-y{syntax:"*";inherits:false;initial-value:0}@property --tw-translate-z{syntax:"*";inherits:false;initial-value:0}@property --tw-scale-x{syntax:"*";inherits:false;initial-value:1}@property --tw-scale-y{syntax:"*";inherits:false;initial-value:1}@property --tw-scale-z{syntax:"*";inherits:false;initial-value:1}@property --tw-rotate-x{syntax:"*";inherits:false}@property --tw-rotate-y{syntax:"*";inherits:false}@property --tw-rotate-z{syntax:"*";inherits:false}@property --tw-skew-x{syntax:"*";inherits:false}@property --tw-skew-y{syntax:"*";inherits:false}@property --tw-pan-x{syntax:"*";inherits:false}@property --tw-pan-y{syntax:"*";inherits:false}@property --tw-pinch-zoom{syntax:"*";inherits:false}@property --tw-space-y-reverse{syntax:"*";inherits:false;initial-value:0}@property --tw-space-x-reverse{syntax:"*";inherits:false;initial-value:0}@property --tw-divide-x-reverse{syntax:"*";inherits:false;initial-value:0}@property --tw-border-style{syntax:"*";inherits:false;initial-value:solid}@property --tw-divide-y-reverse{syntax:"*";inherits:false;initial-value:0}@property --tw-gradient-position{syntax:"*";inherits:false}@property --tw-gradient-from{syntax:"<color>";inherits:false;initial-value:#0000}@property --tw-gradient-via{syntax:"<color>";inherits:false;initial-value:#0000}@property --tw-gradient-to{syntax:"<color>";inherits:false;initial-value:#0000}@property --tw-gradient-stops{syntax:"*";inherits:false}@property --tw-gradient-via-stops{syntax:"*";inherits:false}@property --tw-gradient-from-position{syntax:"<length-percentage>";inherits:false;initial-value:0%}@property --tw-gradient-via-position{syntax:"<length-percentage>";inherits:false;initial-value:50%}@property --tw-gradient-to-position{syntax:"<length-percentage>";inherits:false;initial-value:100%}@property --tw-leading{syntax:"*";inherits:false}@property --tw-font-weight{syntax:"*";inherits:false}@property --tw-tracking{syntax:"*";inherits:false}@property --tw-ordinal{syntax:"*";inherits:false}@property --tw-slashed-zero{syntax:"*";inherits:false}@property --tw-numeric-figure{syntax:"*";inherits:false}@property --tw-numeric-spacing{syntax:"*";inherits:false}@property --tw-numeric-fraction{syntax:"*";inherits:false}@property --tw-shadow{syntax:"*";inherits:false;initial-value:0 0 #0000}@property --tw-shadow-color{syntax:"*";inherits:false}@property --tw-shadow-alpha{syntax:"<percentage>";inherits:false;initial-value:100%}@property --tw-inset-shadow{syntax:"*";inherits:false;initial-value:0 0 #0000}@property --tw-inset-shadow-color{syntax:"*";inherits:false}@property --tw-inset-shadow-alpha{syntax:"<percentage>";inherits:false;initial-value:100%}@property --tw-ring-color{syntax:"*";inherits:false}@property --tw-ring-shadow{syntax:"*";inherits:false;initial-value:0 0 #0000}@property --tw-inset-ring-color{syntax:"*";inherits:false}@property --tw-inset-ring-shadow{syntax:"*";inherits:false;initial-value:0 0 #0000}@property --tw-ring-inset{syntax:"*";inherits:false}@property --tw-ring-offset-width{syntax:"<length>";inherits:false;initial-value:0}@property --tw-ring-offset-color{syntax:"*";inherits:false;initial-value:#fff}@property --tw-ring-offset-shadow{syntax:"*";inherits:false;initial-value:0 0 #0000}@property --tw-outline-style{syntax:"*";inherits:false;initial-value:solid}@property --tw-blur{syntax:"*";inherits:false}@property --tw-brightness{syntax:"*";inherits:false}@property --tw-contrast{syntax:"*";inherits:false}@property --tw-grayscale{syntax:"*";inherits:false}@property --tw-hue-rotate{syntax:"*";inherits:false}@property --tw-invert{syntax:"*";inherits:false}@property --tw-opacity{syntax:"*";inherits:false}@property --tw-saturate{syntax:"*";inherits:false}@property --tw-sepia{syntax:"*";inherits:false}@property --tw-drop-shadow{syntax:"*";inherits:false}@property --tw-drop-shadow-color{syntax:"*";inherits:false}@property --tw-drop-shadow-alpha{syntax:"<percentage>";inherits:false;initial-value:100%}@property --tw-drop-shadow-size{syntax:"*";inherits:false}@property --tw-backdrop-blur{syntax:"*";inherits:false}@property --tw-backdrop-brightness{syntax:"*";inherits:false}@property --tw-backdrop-contrast{syntax:"*";inherits:false}@property --tw-backdrop-grayscale{syntax:"*";inherits:false}@property --tw-backdrop-hue-rotate{syntax:"*";inherits:false}@property --tw-backdrop-invert{syntax:"*";inherits:false}@property --tw-backdrop-opacity{syntax:"*";inherits:false}@property --tw-backdrop-saturate{syntax:"*";inherits:false}@property --tw-backdrop-sepia{syntax:"*";inherits:false}@keyframes spin{to{transform:rotate(360deg)}}@keyframes enter{0%{opacity:var(--tw-enter-opacity,1);transform:translate3d(var(--tw-enter-translate-x,0),var(--tw-enter-translate-y,0),0)scale3d(var(--tw-enter-scale,1),var(--tw-enter-scale,1),var(--tw-enter-scale,1))rotate(var(--tw-enter-rotate,0));filter:blur(var(--tw-enter-blur,0))}}@keyframes exit{to{opacity:var(--tw-exit-opacity,1);transform:translate3d(var(--tw-exit-translate-x,0),var(--tw-exit-translate-y,0),0)scale3d(var(--tw-exit-scale,1),var(--tw-exit-scale,1),var(--tw-exit-scale,1))rotate(var(--tw-exit-rotate,0));filter:blur(var(--tw-exit-blur,0))}}

frontend/dist/index.html

@@ -16,8 +16,8 @@
       href="https://fonts.googleapis.com/css2?family=Hanken+Grotesk:wght@400;500;600&family=JetBrains+Mono:wght@400;500;600&display=swap"
       rel="stylesheet"
     />
-    <script type="module" crossorigin src="/assets/index-CLWJR9Mi.js"></script>
-    <link rel="stylesheet" crossorigin href="/assets/index-DpGaJEG3.css">
+    <script type="module" crossorigin src="/assets/index-CiwitQvv.js"></script>
+    <link rel="stylesheet" crossorigin href="/assets/index-DbWTYGKW.css">
   </head>
   <body>
     <div id="root"></div>

frontend/src/lib/dashboard.tsx

@@ -19,7 +19,7 @@ import type {
   ValidationResult,
 } from "./types"
 
-const DEFAULT_CLASSES = "person, cat, dog, car, chair, cup"
+const DEFAULT_CLASSES = "person, cat, car, chair, cup"
 const DETECTOR_MODEL_VALUES = new Set([
   "yoloe-26n-seg.pt",
   "yoloe-26s-seg.pt",
@@ -46,7 +46,7 @@ const DEFAULT_RULES = `rules:
 const DEFAULT_PARAMS: DetectParams = {
   classes: DEFAULT_CLASSES,
   confidence: 0.25,
-  sampleIntervalSec: 1.0,
+  sampleIntervalSec: 0.25,
   maxFrames: 120,
   modelName: "yoloe-26s-seg.pt",
   imageSize: 640,
@@ -70,6 +70,7 @@ interface DashboardState {
   setParam: <K extends keyof DetectParams>(key: K, value: DetectParams[K]) => void
   rulesText: string
   setRulesText: (text: string) => void
+  replaceRulesText: (text: string, persist?: boolean) => Promise<ValidationResult>
   videoFile: File | null
   videoPreviewUrl: string | null
   setVideo: (file: File | null) => void
@@ -121,6 +122,17 @@ export function DashboardProvider({ children }: { children: ReactNode }) {
     return nextValidation
   }, [])
 
+  const replaceRulesText = useCallback(
+    async (nextRulesText: string, persist = false) => {
+      const nextValidation = await applyRulesText(nextRulesText)
+      if (persist && nextValidation.ok) {
+        await api.saveRules(nextRulesText)
+      }
+      return nextValidation
+    },
+    [applyRulesText],
+  )
+
   // Hydrate defaults from backend config + saved rules on mount.
   useEffect(() => {
     api
@@ -314,6 +326,7 @@ export function DashboardProvider({ children }: { children: ReactNode }) {
       setParam,
       rulesText,
       setRulesText,
+      replaceRulesText,
       videoFile,
       videoPreviewUrl,
       setVideo,
@@ -337,6 +350,7 @@ export function DashboardProvider({ children }: { children: ReactNode }) {
       params,
       setParam,
       rulesText,
+      replaceRulesText,
       videoFile,
       videoPreviewUrl,
       setVideo,

frontend/src/lib/types.ts

@@ -50,10 +50,91 @@ export interface RuleSummary {
   actions: { type: string; name: string }[]
 }
 
+export type TriggerMode = "while" | "enter" | "exit" | "change"
+export type ActionType = "simulate" | "webhook"
+
+export interface PresentCondition {
+  label: string
+  min_count: number
+}
+
+export interface CountCondition {
+  label: string
+  minimum: number
+}
+
+export interface NearCondition {
+  a: string
+  b: string
+  max_gap_percent: number
+}
+
+export interface FarCondition {
+  a: string
+  b: string
+  min_gap_percent: number
+}
+
+export interface MovingCondition {
+  label: string
+  min_displacement_ratio: number
+  window_frames: number
+  max_missing_frames: number
+}
+
+export interface CooldownCondition {
+  key: string | null
+  seconds: number | null
+  minutes: number | null
+}
+
+export interface ConditionBlock {
+  present?: PresentCondition | null
+  count?: CountCondition | null
+  near?: NearCondition | null
+  far?: FarCondition | null
+  moving?: MovingCondition | null
+  cooldown?: CooldownCondition | null
+}
+
+export interface WhenClause {
+  all: ConditionBlock[]
+  any: ConditionBlock[]
+}
+
+export interface ActionSpec {
+  type: ActionType
+  name: string
+  url: string | null
+  payload: Record<string, unknown>
+}
+
+export interface ActionSet {
+  enter: ActionSpec[]
+  exit: ActionSpec[]
+  while: ActionSpec[]
+}
+
+export interface AutomationRule {
+  name: string
+  when: WhenClause
+  trigger: { on: TriggerMode }
+  gate: {
+    enabled: boolean
+    cooldown: CooldownCondition | null
+  }
+  then: ActionSpec[] | ActionSet
+}
+
+export interface AutomationDocument {
+  rules: AutomationRule[]
+}
+
 export interface ValidationResult {
   ok: boolean
   error: string | null
   rules: RuleSummary[]
+  document: AutomationDocument | null
 }
 
 export interface CompileResult {

frontend/src/modules/detector/DetectorPanel.tsx

@@ -114,7 +114,7 @@ export function DetectorPanel() {
           value={params.classes}
           onChange={(e) => setParam("classes", e.target.value)}
           className="min-h-16 font-mono text-xs leading-relaxed"
-          placeholder="person, cat, dog, car, chair, cup…"
+          placeholder="person, cat, car, chair, cup…"
         />
       </div>
 
@@ -147,11 +147,11 @@ export function DetectorPanel() {
         <SliderRow
           label="Sample interval"
           value={params.sampleIntervalSec}
-          display={`${params.sampleIntervalSec.toFixed(1)}s`}
-          min={0.1}
+          display={`${params.sampleIntervalSec.toFixed(2)}s`}
+          min={0.25}
           max={5}
-          step={0.1}
-          onChange={(v) => setParam("sampleIntervalSec", Number(v.toFixed(1)))}
+          step={0.25}
+          onChange={(v) => setParam("sampleIntervalSec", Number(v.toFixed(2)))}
         />
         <SliderRow
           label="Max frames"

frontend/src/modules/rules/RuleStudioPanel.tsx

@@ -1,6 +1,7 @@
 import { useState } from "react"
 import {
   CheckCircle2,
+  ChevronDown,
   CircleSlash,
   Loader2,
   Save,
@@ -11,17 +12,26 @@ import {
 } from "lucide-react"
 import { useDashboard } from "@/lib/dashboard"
 import { Button } from "@/components/ui/button"
+import { Input } from "@/components/ui/input"
 import { Textarea } from "@/components/ui/textarea"
 import { Badge } from "@/components/ui/badge"
 import { Tabs, TabsContent, TabsList, TabsTrigger } from "@/components/ui/tabs"
 import { Label } from "@/components/ui/label"
 import { Switch } from "@/components/ui/switch"
 import { cn } from "@/lib/utils"
+import type {
+  ActionSpec,
+  AutomationDocument,
+  AutomationRule,
+  ConditionBlock,
+  CooldownCondition,
+} from "@/lib/types"
 
 export function RuleStudioPanel() {
   const {
     rulesText,
     setRulesText,
+    replaceRulesText,
     validate,
     validation,
     save,
@@ -35,6 +45,21 @@ export function RuleStudioPanel() {
     "When a cat appears in the image, notify me.",
   )
   const [armedDelete, setArmedDelete] = useState<string | null>(null)
+  const [expandedRule, setExpandedRule] = useState<string | null>(null)
+  const document = validation?.ok ? validation.document : null
+
+  const applyDocument = (nextDocument: AutomationDocument) => {
+    void replaceRulesText(JSON.stringify(nextDocument, null, 2), true)
+  }
+
+  const updateRule = (ruleIndex: number, updater: (rule: AutomationRule) => void) => {
+    if (!document) return
+    const nextDocument = cloneDocument(document)
+    const rule = nextDocument.rules[ruleIndex]
+    if (!rule) return
+    updater(rule)
+    applyDocument(nextDocument)
+  }
 
   return (
     <Tabs defaultValue="compose">
@@ -127,69 +152,97 @@ export function RuleStudioPanel() {
 
       {/* Rules: parsed view */}
       <TabsContent value="rules">
-        {validation?.ok && validation.rules.length > 0 ? (
+        {validation?.ok && document && document.rules.length > 0 ? (
           <div className="space-y-2">
-            {validation.rules.map((r) => (
+            {document.rules.map((rule, ruleIndex) => {
+              const r = validation.rules.find((item) => item.name === rule.name) ?? {
+                name: rule.name,
+                enabled: rule.gate.enabled,
+                trigger: rule.trigger.on,
+                labels: [],
+                conditions: rule.when.all.length + rule.when.any.length,
+                actions: ruleActions(rule).map((action) => ({ type: action.type, name: action.name })),
+              }
+              const expanded = expandedRule === rule.name
+              return (
               <div
                 key={r.name}
-                className="flex flex-col gap-3 rounded-lg border border-border bg-black/20 px-4 py-3 sm:flex-row sm:items-center sm:justify-between"
+                className="rounded-lg border border-border bg-black/20"
               >
-                <div className="min-w-0">
-                  <div className="flex items-center gap-2">
-                    <span
-                      className={cn(
-                        "size-1.5 rounded-full",
-                        r.enabled ? "bg-primary" : "bg-muted-foreground/40",
-                      )}
-                    />
-                    <span className="truncate font-mono text-sm text-foreground">{r.name}</span>
-                    {r.name.startsWith("example-") && <Badge variant="default">example</Badge>}
-                    <Badge variant="default">{r.trigger}</Badge>
+                <button
+                  type="button"
+                  className="flex w-full flex-col gap-3 px-4 py-3 text-left sm:flex-row sm:items-center sm:justify-between"
+                  onClick={() => setExpandedRule(expanded ? null : rule.name)}
+                >
+                  <div className="min-w-0">
+                    <div className="flex items-center gap-2">
+                      <ChevronDown
+                        className={cn(
+                          "size-4 shrink-0 text-muted-foreground transition-transform",
+                          expanded && "rotate-180 text-primary",
+                        )}
+                      />
+                      <span
+                        className={cn(
+                          "size-1.5 rounded-full",
+                          r.enabled ? "bg-primary" : "bg-muted-foreground/40",
+                        )}
+                      />
+                      <span className="truncate font-mono text-sm text-foreground">{r.name}</span>
+                      {r.name.startsWith("example-") && <Badge variant="default">example</Badge>}
+                      <Badge variant="default">{r.trigger}</Badge>
+                    </div>
+                    <div className="mt-1 pl-9 text-xs text-muted-foreground">
+                      {r.conditions} condition{r.conditions === 1 ? "" : "s"} →{" "}
+                      {r.actions.map((a) => `${a.type}:${a.name}`).join(", ")}
+                    </div>
+                    {r.labels.length > 0 && (
+                      <div className="mt-2 flex flex-wrap gap-1 pl-9">
+                        {r.labels.map((label) => (
+                          <span
+                            key={label}
+                            className="rounded border border-border bg-black/30 px-1.5 py-0.5 font-mono text-[0.625rem] text-muted-foreground"
+                          >
+                            {label}
+                          </span>
+                        ))}
+                      </div>
+                    )}
                   </div>
-                  <div className="mt-1 pl-3.5 text-xs text-muted-foreground">
-                    {r.conditions} condition{r.conditions === 1 ? "" : "s"} →{" "}
-                    {r.actions.map((a) => `${a.type}:${a.name}`).join(", ")}
+                  <div className="flex shrink-0 items-center gap-2" onClick={(event) => event.stopPropagation()}>
+                    <Label htmlFor={`enabled-${r.name}`} className="text-xs text-muted-foreground">
+                      {r.enabled ? "enabled" : "disabled"}
+                    </Label>
+                    <Switch
+                      id={`enabled-${r.name}`}
+                      checked={r.enabled}
+                      onCheckedChange={(checked) => setRuleEnabled(r.name, checked)}
+                    />
+                    <Button
+                      variant={armedDelete === r.name ? "destructive" : "secondary"}
+                      size="sm"
+                      onClick={() => {
+                        if (armedDelete === r.name) {
+                          void deleteRule(r.name)
+                          setArmedDelete(null)
+                        } else {
+                          setArmedDelete(r.name)
+                        }
+                      }}
+                    >
+                      <Trash2 className="size-4" />
+                      {armedDelete === r.name ? "Confirm" : "Delete"}
+                    </Button>
                   </div>
-                  {r.labels.length > 0 && (
-                    <div className="mt-2 flex flex-wrap gap-1 pl-3.5">
-                      {r.labels.map((label) => (
-                        <span
-                          key={label}
-                          className="rounded border border-border bg-black/30 px-1.5 py-0.5 font-mono text-[0.625rem] text-muted-foreground"
-                        >
-                          {label}
-                        </span>
-                      ))}
-                    </div>
-                  )}
-                </div>
-                <div className="flex shrink-0 items-center gap-2">
-                  <Label htmlFor={`enabled-${r.name}`} className="text-xs text-muted-foreground">
-                    {r.enabled ? "enabled" : "disabled"}
-                  </Label>
-                  <Switch
-                    id={`enabled-${r.name}`}
-                    checked={r.enabled}
-                    onCheckedChange={(checked) => setRuleEnabled(r.name, checked)}
+                </button>
+                {expanded && (
+                  <RuleEditor
+                    rule={rule}
+                    onUpdate={(updater) => updateRule(ruleIndex, updater)}
                   />
-                  <Button
-                    variant={armedDelete === r.name ? "destructive" : "secondary"}
-                    size="sm"
-                    onClick={() => {
-                      if (armedDelete === r.name) {
-                        void deleteRule(r.name)
-                        setArmedDelete(null)
-                      } else {
-                        setArmedDelete(r.name)
-                      }
-                    }}
-                  >
-                    <Trash2 className="size-4" />
-                    {armedDelete === r.name ? "Confirm" : "Delete"}
-                  </Button>
-                </div>
+                )}
               </div>
-            ))}
+            )})}
           </div>
         ) : validation?.ok ? (
           <div className="flex flex-col items-center gap-2 py-8 text-center text-muted-foreground">
@@ -209,3 +262,329 @@ export function RuleStudioPanel() {
     </Tabs>
   )
 }
+
+function RuleEditor({
+  rule,
+  onUpdate,
+}: {
+  rule: AutomationRule
+  onUpdate: (updater: (rule: AutomationRule) => void) => void
+}) {
+  return (
+    <div className="border-t border-border px-4 pb-4 pt-3">
+      <div className="grid gap-3 lg:grid-cols-2">
+        <div className="space-y-3">
+          <div className="flex items-center justify-between rounded-md border border-border bg-black/20 px-3 py-2">
+            <div>
+              <div className="text-xs font-medium text-foreground">Cooldown</div>
+              <div className="text-[0.6875rem] text-muted-foreground">Blocks repeated fires for this rule.</div>
+            </div>
+            <Switch
+              checked={Boolean(rule.gate.cooldown)}
+              onCheckedChange={(checked) =>
+                onUpdate((draft) => {
+                  draft.gate.cooldown = checked ? { key: draft.name, seconds: null, minutes: 5 } : null
+                })
+              }
+            />
+          </div>
+          {rule.gate.cooldown && (
+            <CooldownEditor
+              cooldown={rule.gate.cooldown}
+              onChange={(next) => onUpdate((draft) => { draft.gate.cooldown = next })}
+            />
+          )}
+          <ConditionList
+            title="All conditions"
+            conditions={rule.when.all}
+            onUpdate={(conditionIndex, updater) =>
+              onUpdate((draft) => {
+                const condition = draft.when.all[conditionIndex]
+                if (condition) updater(condition)
+              })
+            }
+          />
+          {rule.when.any.length > 0 && (
+            <ConditionList
+              title="Any conditions"
+              conditions={rule.when.any}
+              onUpdate={(conditionIndex, updater) =>
+                onUpdate((draft) => {
+                  const condition = draft.when.any[conditionIndex]
+                  if (condition) updater(condition)
+                })
+              }
+            />
+          )}
+        </div>
+        <div className="space-y-3">
+          <ActionList
+            rule={rule}
+            onUpdate={(bucket, actionIndex, updater) =>
+              onUpdate((draft) => {
+                updateRuleAction(draft, bucket, actionIndex, updater)
+              })
+            }
+          />
+          <div className="rounded-md border border-border bg-black/20 p-3 text-[0.6875rem] text-muted-foreground">
+            Global webhooks still act as the master safety switch. Per-rule webhook actions can carry their own URL.
+          </div>
+        </div>
+      </div>
+    </div>
+  )
+}
+
+function ConditionList({
+  title,
+  conditions,
+  onUpdate,
+}: {
+  title: string
+  conditions: ConditionBlock[]
+  onUpdate: (conditionIndex: number, updater: (condition: ConditionBlock) => void) => void
+}) {
+  return (
+    <div className="space-y-2">
+      <div className="font-mono text-[0.625rem] uppercase tracking-wider text-muted-foreground">{title}</div>
+      {conditions.map((condition, index) => (
+        <ConditionEditor
+          key={index}
+          condition={condition}
+          onUpdate={(updater) => onUpdate(index, updater)}
+        />
+      ))}
+    </div>
+  )
+}
+
+function ConditionEditor({
+  condition,
+  onUpdate,
+}: {
+  condition: ConditionBlock
+  onUpdate: (updater: (condition: ConditionBlock) => void) => void
+}) {
+  if (condition.present) {
+    return (
+      <EditorBlock label="present">
+        <ReadOnlyField label="label" value={condition.present.label} />
+        <NumberField label="min count" value={condition.present.min_count} min={1} step={1} onChange={(value) => onUpdate((draft) => { draft.present!.min_count = Math.max(1, Math.round(value)) })} />
+      </EditorBlock>
+    )
+  }
+  if (condition.count) {
+    return (
+      <EditorBlock label="count">
+        <ReadOnlyField label="label" value={condition.count.label} />
+        <NumberField label="minimum" value={condition.count.minimum} min={1} step={1} onChange={(value) => onUpdate((draft) => { draft.count!.minimum = Math.max(1, Math.round(value)) })} />
+      </EditorBlock>
+    )
+  }
+  if (condition.near) {
+    return (
+      <EditorBlock label="near">
+        <ReadOnlyField label="a" value={condition.near.a} />
+        <ReadOnlyField label="b" value={condition.near.b} />
+        <NumberField label="max gap %" value={condition.near.max_gap_percent} min={0} step={1} onChange={(value) => onUpdate((draft) => { draft.near!.max_gap_percent = value })} />
+      </EditorBlock>
+    )
+  }
+  if (condition.far) {
+    return (
+      <EditorBlock label="far">
+        <ReadOnlyField label="a" value={condition.far.a} />
+        <ReadOnlyField label="b" value={condition.far.b} />
+        <NumberField label="min gap %" value={condition.far.min_gap_percent} min={0} step={1} onChange={(value) => onUpdate((draft) => { draft.far!.min_gap_percent = value })} />
+      </EditorBlock>
+    )
+  }
+  if (condition.moving) {
+    return (
+      <EditorBlock label="moving">
+        <ReadOnlyField label="label" value={condition.moving.label} />
+        <NumberField label="min move (×size)" value={condition.moving.min_displacement_ratio} min={0} step={0.05} onChange={(value) => onUpdate((draft) => { draft.moving!.min_displacement_ratio = value })} />
+        <NumberField label="frames" value={condition.moving.window_frames} min={3} step={1} onChange={(value) => onUpdate((draft) => { draft.moving!.window_frames = Math.max(3, Math.round(value)) })} />
+        <NumberField label="misses" value={condition.moving.max_missing_frames} min={0} step={1} onChange={(value) => onUpdate((draft) => { draft.moving!.max_missing_frames = Math.max(0, Math.round(value)) })} />
+      </EditorBlock>
+    )
+  }
+  if (condition.cooldown) {
+    return (
+      <EditorBlock label="cooldown">
+        <CooldownEditor cooldown={condition.cooldown} onChange={(next) => onUpdate((draft) => { draft.cooldown = next })} />
+      </EditorBlock>
+    )
+  }
+  return null
+}
+
+function CooldownEditor({
+  cooldown,
+  onChange,
+}: {
+  cooldown: CooldownCondition
+  onChange: (next: CooldownCondition) => void
+}) {
+  return (
+    <div className="grid gap-2 sm:grid-cols-3">
+      <TextField label="key" value={cooldown.key ?? ""} onChange={(value) => onChange({ ...cooldown, key: value || null })} />
+      <OptionalNumberField label="seconds" value={cooldown.seconds} onChange={(value) => onChange({ ...cooldown, seconds: value, minutes: null })} />
+      <OptionalNumberField label="minutes" value={cooldown.minutes} onChange={(value) => onChange({ ...cooldown, minutes: value, seconds: null })} />
+    </div>
+  )
+}
+
+function ActionList({
+  rule,
+  onUpdate,
+}: {
+  rule: AutomationRule
+  onUpdate: (bucket: ActionBucket, actionIndex: number, updater: (action: ActionSpec) => void) => void
+}) {
+  const actions = editableActions(rule)
+  return (
+    <div className="space-y-2">
+      <div className="font-mono text-[0.625rem] uppercase tracking-wider text-muted-foreground">Actions</div>
+      {actions.map(({ action, bucket, actionIndex, label }) => (
+        <EditorBlock key={`${bucket}-${actionIndex}`} label={label}>
+          <TextField label="name" value={action.name} onChange={(value) => onUpdate(bucket, actionIndex, (draft) => { draft.name = value })} />
+          <div className="space-y-1">
+            <Label className="text-[0.6875rem] text-muted-foreground">type</Label>
+            <select
+              value={action.type}
+              onChange={(event) => onUpdate(bucket, actionIndex, (draft) => { draft.type = event.target.value as ActionSpec["type"] })}
+              className="h-9 w-full rounded-md border border-input bg-black/20 px-3 text-sm text-foreground focus-visible:outline-none focus-visible:border-primary/50 focus-visible:ring-2 focus-visible:ring-ring"
+            >
+              <option value="simulate">simulate</option>
+              <option value="webhook">webhook</option>
+            </select>
+          </div>
+          {action.type === "webhook" && (
+            <TextField label="url" value={action.url ?? ""} onChange={(value) => onUpdate(bucket, actionIndex, (draft) => { draft.url = value || null })} />
+          )}
+        </EditorBlock>
+      ))}
+    </div>
+  )
+}
+
+function EditorBlock({ label, children }: { label: string; children: React.ReactNode }) {
+  return (
+    <div className="rounded-md border border-border bg-black/20 p-3">
+      <div className="mb-2 font-mono text-[0.625rem] uppercase tracking-wider text-primary">{label}</div>
+      <div className="grid gap-2 sm:grid-cols-2">{children}</div>
+    </div>
+  )
+}
+
+function TextField({ label, value, onChange }: { label: string; value: string; onChange: (value: string) => void }) {
+  return (
+    <div className="space-y-1">
+      <Label className="text-[0.6875rem] text-muted-foreground">{label}</Label>
+      <Input value={value} onChange={(event) => onChange(event.target.value)} />
+    </div>
+  )
+}
+
+function ReadOnlyField({ label, value }: { label: string; value: string }) {
+  return (
+    <div className="space-y-1">
+      <Label className="text-[0.6875rem] text-muted-foreground">{label}</Label>
+      <div className="flex h-9 items-center rounded-md border border-border bg-black/30 px-3 font-mono text-xs text-muted-foreground">
+        {value}
+      </div>
+    </div>
+  )
+}
+
+function NumberField({
+  label,
+  value,
+  min,
+  step,
+  onChange,
+}: {
+  label: string
+  value: number
+  min?: number
+  step?: number
+  onChange: (value: number) => void
+}) {
+  return (
+    <div className="space-y-1">
+      <Label className="text-[0.6875rem] text-muted-foreground">{label}</Label>
+      <Input
+        type="number"
+        value={Number.isFinite(value) ? value : 0}
+        min={min}
+        step={step}
+        onChange={(event) => onChange(Number(event.target.value || 0))}
+      />
+    </div>
+  )
+}
+
+function OptionalNumberField({
+  label,
+  value,
+  onChange,
+}: {
+  label: string
+  value: number | null
+  onChange: (value: number | null) => void
+}) {
+  return (
+    <div className="space-y-1">
+      <Label className="text-[0.6875rem] text-muted-foreground">{label}</Label>
+      <Input
+        type="number"
+        value={value ?? ""}
+        min={0}
+        step={1}
+        onChange={(event) => onChange(event.target.value === "" ? null : Number(event.target.value))}
+      />
+    </div>
+  )
+}
+
+type ActionBucket = "then" | "enter" | "exit" | "while"
+
+function editableActions(rule: AutomationRule): Array<{
+  bucket: ActionBucket
+  actionIndex: number
+  label: string
+  action: ActionSpec
+}> {
+  if (Array.isArray(rule.then)) {
+    return rule.then.map((action, actionIndex) => ({ bucket: "then", actionIndex, label: "then", action }))
+  }
+  return [
+    ...rule.then.enter.map((action, actionIndex) => ({ bucket: "enter" as const, actionIndex, label: "enter", action })),
+    ...rule.then.exit.map((action, actionIndex) => ({ bucket: "exit" as const, actionIndex, label: "exit", action })),
+    ...rule.then.while.map((action, actionIndex) => ({ bucket: "while" as const, actionIndex, label: "while", action })),
+  ]
+}
+
+function ruleActions(rule: AutomationRule): ActionSpec[] {
+  return editableActions(rule).map((item) => item.action)
+}
+
+function updateRuleAction(
+  rule: AutomationRule,
+  bucket: ActionBucket,
+  actionIndex: number,
+  updater: (action: ActionSpec) => void,
+) {
+  if (Array.isArray(rule.then)) {
+    const action = rule.then[actionIndex]
+    if (action) updater(action)
+    return
+  }
+  const action = rule.then[bucket === "then" ? "enter" : bucket][actionIndex]
+  if (action) updater(action)
+}
+
+function cloneDocument(document: AutomationDocument): AutomationDocument {
+  return JSON.parse(JSON.stringify(document)) as AutomationDocument
+}

server.py

@@ -13,6 +13,7 @@ does not require torch/ultralytics.
 from __future__ import annotations
 
 import os
+import logging
 from pathlib import Path
 from typing import Any
 
@@ -50,6 +51,11 @@ RENDERS = ROOT / ".local" / "renders"
 RENDERS.mkdir(parents=True, exist_ok=True)
 
 DEFAULT_MODEL = "yoloe-26s-seg.pt"
+logging.basicConfig(
+    level=os.environ.get("TINY_TRIGGER_LOG_LEVEL", "INFO").upper(),
+    format="%(asctime)s %(levelname)s [%(name)s] %(message)s",
+)
+LOGGER = logging.getLogger(__name__)
 
 
 # ── serialization helpers (JSON-shaped, for the custom frontend) ─────────────
@@ -120,6 +126,18 @@ def detect_and_automate(
 
     rules = load_automation_text(rules_text)
     detection_classes = _merge_class_names(parse_class_prompt(classes), document_labels(rules))
+    tracking_enabled = _document_uses_moving(rules)
+    LOGGER.info(
+        "Starting detect_and_automate video=%s classes=%s rules=%s sample_interval=%s max_frames=%s model=%s image_size=%s tracking=%s",
+        Path(video_path).name,
+        ", ".join(detection_classes),
+        len(rules.rules),
+        sample_interval_sec,
+        max_frames,
+        model_name or DEFAULT_MODEL,
+        image_size or "default",
+        tracking_enabled,
+    )
 
     result = process_video(
         video_path=video_path,
@@ -132,8 +150,15 @@ def detect_and_automate(
         image_size=image_size or None,
         device=None if device in ("", "auto") else device,
         max_detections=max_detections or None,
+        tracking_enabled=tracking_enabled,
         output_dir=str(RENDERS),
     )
+    LOGGER.info(
+        "Detection complete: sampled_frames=%s detections=%s annotated=%s",
+        result.processed_frames,
+        len(result.detections),
+        result.output_video_path,
+    )
 
     events, _last_fired = evaluate_video_detections(
         rules.rules,
@@ -147,6 +172,7 @@ def detect_and_automate(
         events, enable_webhooks=enable_webhooks, webhook_url=webhook_url or None
     )
     append_events(dispatched)
+    LOGGER.info("Automation evaluation complete: events=%s dispatched=%s", len(events), len(dispatched))
 
     automation_path = render_automation_video(
         source_video_path=video_path,
@@ -156,6 +182,7 @@ def detect_and_automate(
         max_frames=max_frames,
         output_dir=str(RENDERS),
     )
+    LOGGER.info("detect_and_automate complete: output=%s", automation_path)
 
     fired = len(dispatched)
     return {
@@ -249,10 +276,11 @@ def validate_rules(rules_text: str) -> dict:
     try:
         document = load_automation_text(rules_text)
     except Exception as exc:  # noqa: BLE001 — surface any validation error to the UI
-        return {"ok": False, "error": str(exc), "rules": []}
+        return {"ok": False, "error": str(exc), "rules": [], "document": None}
     return {
         "ok": True,
         "error": None,
+        "document": document.model_dump(mode="json", by_alias=True),
         "rules": [
             {
                 "name": r.name,
@@ -355,6 +383,14 @@ def _merge_class_names(*groups: list[str]) -> list[str]:
     return merged
 
 
+def _document_uses_moving(document: AutomationDocument) -> bool:
+    for rule in document.rules:
+        for condition in [*rule.when.all_conditions, *rule.when.any_conditions]:
+            if condition.moving is not None:
+                return True
+    return False
+
+
 # ── static frontend + media (custom routes take priority over gradio's) ──────
 @app.get("/", response_class=HTMLResponse)
 def index() -> Any:

tests/test_automation.py

@@ -138,7 +138,7 @@ def test_moving_condition_uses_tracked_centroid_history() -> None:
                         "name": "car-moving",
                         "when": {
                             "all": [
-                                {"moving": {"label": "car", "min_displacement_percent": 3, "window_frames": 3}}
+                                {"moving": {"label": "car", "min_displacement_ratio": 0.15, "window_frames": 3}}
                             ]
                         },
                         "then": [{"type": "simulate", "name": "notify"}],
@@ -170,6 +170,46 @@ def test_moving_condition_uses_tracked_centroid_history() -> None:
     assert [event.action for event in third] == ["notify"]
 
 
+def test_moving_condition_ignores_box_jitter_on_stationary_object() -> None:
+    document = load_automation_text(
+        json.dumps(
+            {
+                "rules": [
+                    {
+                        "name": "person-moving",
+                        "when": {
+                            "all": [
+                                {"moving": {"label": "person", "min_displacement_ratio": 0.15, "window_frames": 3}}
+                            ]
+                        },
+                        "then": [{"type": "simulate", "name": "notify"}],
+                    }
+                ]
+            }
+        )
+    )
+    engine = RuleEngine(document.rules)
+
+    # Stationary person; only the box's top edge flickers (arms/occlusion/shadow),
+    # which swings the geometric center but leaves the foot point essentially put.
+    # The old center-displacement check fired on this; the foot-point + size-ratio
+    # check must not.
+    frames = [
+        (0.45, 0.50, 0.55, 0.80),
+        (0.46, 0.42, 0.56, 0.80),
+        (0.44, 0.34, 0.54, 0.80),
+    ]
+    events = []
+    for index, box in enumerate(frames):
+        events += engine.evaluate_frame(
+            [detection("person", box, frame=index, time=float(index), track_id=1)],
+            frame_index=index,
+            timestamp_sec=float(index),
+        )
+
+    assert events == []
+
+
 def test_moving_condition_migrates_short_window_to_three() -> None:
     document = load_automation_text(
         json.dumps(
@@ -202,7 +242,7 @@ def test_moving_condition_does_not_reenter_after_one_missed_frame() -> None:
                                 {
                                     "moving": {
                                         "label": "car",
-                                        "min_displacement_percent": 3,
+                                        "min_displacement_ratio": 0.15,
                                         "window_frames": 3,
                                         "max_missing_frames": 1,
                                     }

tests/test_detector.py

@@ -1,9 +1,44 @@
 from __future__ import annotations
 
-from tiny_trigger.detector import parse_class_prompt, suppress_duplicate_detections
+from tiny_trigger.detector import (
+    GreedyIoUTracker,
+    UltralyticsYOLOEDetector,
+    detections_from_ultralytics_result,
+    parse_class_prompt,
+    suppress_duplicate_detections,
+)
 from tiny_trigger.models import Detection
 
 
+class TensorLike:
+    def __init__(self, values):
+        self.values = values
+
+    def cpu(self):
+        return self
+
+    def tolist(self):
+        return self.values
+
+
+class BoxesLike:
+    def __init__(self, *, ids=None):
+        self.xyxy = TensorLike([[10.0, 20.0, 30.0, 40.0], [40.0, 50.0, 70.0, 90.0]])
+        self.conf = TensorLike([0.91, 0.82])
+        self.cls = TensorLike([0, 1])
+        self.id = TensorLike(ids) if ids is not None else None
+
+
+class ResultLike:
+    def __init__(self, *, ids=None):
+        self.boxes = BoxesLike(ids=ids)
+        self.names = {0: "person", 1: "car"}
+
+
+class FrameLike:
+    shape = (100, 100, 3)
+
+
 def test_parse_class_prompt_splits_and_dedupes() -> None:
     assert parse_class_prompt(" cat, feeder robot\npackage; cat ") == ["cat", "feeder robot", "package"]
 
@@ -34,3 +69,136 @@ def test_suppress_duplicate_detections_keeps_highest_confidence_same_label_box()
     assert ("person", 0.62) not in [(item.label, item.confidence) for item in filtered]
     assert ("person", 0.80) in [(item.label, item.confidence) for item in filtered]
     assert ("bag", 0.70) in [(item.label, item.confidence) for item in filtered]
+
+
+def test_suppress_duplicate_detections_keeps_oldest_track_id() -> None:
+    def tracked(confidence: float, box: tuple[float, float, float, float], track_id: int) -> Detection:
+        return Detection(
+            frame_index=0,
+            timestamp_sec=0.0,
+            label="person",
+            confidence=confidence,
+            bbox_xyxy=(0.0, 0.0, 10.0, 10.0),
+            bbox_xyxy_norm=box,
+            track_id=track_id,
+        )
+
+    # Higher-confidence box is the newer track (id 7); the suppressed
+    # lower-confidence box is the established track (id 5).
+    detections = [
+        tracked(0.91, (0.11, 0.11, 0.51, 0.51), 7),
+        tracked(0.62, (0.10, 0.10, 0.50, 0.50), 5),
+    ]
+
+    filtered = suppress_duplicate_detections(detections, iou_threshold=0.8)
+
+    assert len(filtered) == 1
+    assert filtered[0].confidence == 0.91  # highest-confidence geometry kept
+    assert filtered[0].track_id == 5  # identity follows the oldest track, not confidence
+
+
+def test_greedy_tracker_matches_overlap_and_keeps_unmatched() -> None:
+    tracker = GreedyIoUTracker(iou_threshold=0.3)
+
+    def det(label: str, box: tuple[float, float, float, float]) -> Detection:
+        return Detection(
+            frame_index=0,
+            timestamp_sec=0.0,
+            label=label,
+            confidence=0.9,
+            bbox_xyxy=(0.0, 0.0, 10.0, 10.0),
+            bbox_xyxy_norm=box,
+        )
+
+    first = tracker.assign([det("person", (0.10, 0.10, 0.20, 0.40))])
+    person_id = first[0].track_id
+    assert person_id is not None
+
+    # Next frame: the same person (overlapping box) keeps its id; a second person
+    # that matches no existing track is still returned with a fresh id, never dropped.
+    second = tracker.assign(
+        [
+            det("person", (0.11, 0.10, 0.21, 0.40)),
+            det("person", (0.70, 0.10, 0.80, 0.40)),
+        ]
+    )
+    ids = [item.track_id for item in second]
+    assert len(second) == 2  # nothing dropped for failing to match
+    assert person_id in ids
+    assert len(set(ids)) == 2  # the unmatched one got its own id
+
+
+def test_detections_from_ultralytics_result_reads_track_ids() -> None:
+    detections = detections_from_ultralytics_result(
+        ResultLike(ids=[7, 8]),
+        frame_shape=(100, 100, 3),
+        frame_index=3,
+        timestamp_sec=1.5,
+        fallback_names=["person", "car"],
+    )
+
+    assert [(item.label, item.track_id) for item in detections] == [("car", 8), ("person", 7)]
+
+
+def test_detections_from_ultralytics_result_allows_missing_track_ids() -> None:
+    detections = detections_from_ultralytics_result(
+        ResultLike(),
+        frame_shape=(100, 100, 3),
+        frame_index=3,
+        timestamp_sec=1.5,
+        fallback_names=["person", "car"],
+    )
+
+    assert [item.track_id for item in detections] == [None, None]
+
+
+def test_ultralytics_detector_uses_predict_by_default() -> None:
+    calls = []
+
+    class ModelLike:
+        def predict(self, frame, **kwargs):
+            calls.append(("predict", frame, kwargs))
+            return [ResultLike()]
+
+    detector = UltralyticsYOLOEDetector.__new__(UltralyticsYOLOEDetector)
+    detector.class_names = ["person", "car"]
+    detector.model_name = "fake.pt"
+    detector.device = None
+    detector.tracking_enabled = False
+    detector._tracker = None
+    detector.model = ModelLike()
+    frame = FrameLike()
+
+    detections = detector.detect(frame, frame_index=1, timestamp_sec=0.5, confidence=0.25)
+
+    assert [item.track_id for item in detections] == [None, None]
+    assert calls == [("predict", frame, {"conf": 0.25, "verbose": False})]
+
+
+def test_ultralytics_detector_predicts_and_assigns_track_ids_when_tracking_enabled() -> None:
+    calls = []
+
+    class ModelLike:
+        def predict(self, frame, **kwargs):
+            calls.append("predict")
+            return [ResultLike()]
+
+    detector = UltralyticsYOLOEDetector.__new__(UltralyticsYOLOEDetector)
+    detector.class_names = ["person", "car"]
+    detector.model_name = "fake.pt"
+    detector.device = None
+    detector.tracking_enabled = True
+    detector._tracker = GreedyIoUTracker()
+    detector.model = ModelLike()
+    frame = FrameLike()
+
+    first = detector.detect(frame, frame_index=1, timestamp_sec=0.5, confidence=0.25)
+    second = detector.detect(frame, frame_index=2, timestamp_sec=1.0, confidence=0.25)
+
+    # Detection always runs through predict (never track); IDs come from our tracker.
+    assert calls == ["predict", "predict"]
+    first_ids = {item.label: item.track_id for item in first}
+    assert all(track_id is not None for track_id in first_ids.values())
+    # Same boxes the next frame -> same IDs (stable identity, nothing dropped).
+    second_ids = {item.label: item.track_id for item in second}
+    assert second_ids == first_ids

tests/test_server.py

@@ -2,7 +2,7 @@ from __future__ import annotations
 
 import json
 
-from server import _merge_class_names, _merge_documents
+from server import _document_uses_moving, _merge_class_names, _merge_documents
 from tiny_trigger.automation import AutomationDocument, load_automation_text
 
 
@@ -47,3 +47,35 @@ def test_merge_class_names_adds_rule_labels_without_duplicates() -> None:
         "Monitor",
         "guitar",
     ]
+
+
+def test_document_uses_moving_only_for_motion_rules() -> None:
+    presence = load_automation_text(
+        json.dumps(
+            {
+                "rules": [
+                    {
+                        "name": "person-visible",
+                        "when": {"all": [{"present": {"label": "person"}}]},
+                        "then": [{"type": "simulate", "name": "notify"}],
+                    }
+                ]
+            }
+        )
+    )
+    moving = load_automation_text(
+        json.dumps(
+            {
+                "rules": [
+                    {
+                        "name": "car-moving",
+                        "when": {"all": [{"moving": {"label": "car"}}]},
+                        "then": [{"type": "simulate", "name": "notify"}],
+                    }
+                ]
+            }
+        )
+    )
+
+    assert not _document_uses_moving(presence)
+    assert _document_uses_moving(moving)

tests/test_tracking.py

@@ -1,26 +0,0 @@
-from __future__ import annotations
-
-from tiny_trigger.models import Detection
-from tiny_trigger.tracking import SimpleTracker
-
-
-def detection(label: str, box: tuple[float, float, float, float]) -> Detection:
-    return Detection(
-        frame_index=0,
-        timestamp_sec=0.0,
-        label=label,
-        confidence=0.9,
-        bbox_xyxy=(0.0, 0.0, 10.0, 10.0),
-        bbox_xyxy_norm=box,
-    )
-
-
-def test_simple_tracker_keeps_same_label_track_id() -> None:
-    tracker = SimpleTracker(max_match_distance_percent=20)
-
-    first = tracker.update([detection("car", (0.10, 0.10, 0.20, 0.20))])
-    second = tracker.update([detection("car", (0.15, 0.10, 0.25, 0.20))])
-    third = tracker.update([detection("person", (0.15, 0.10, 0.25, 0.20))])
-
-    assert first[0].track_id == second[0].track_id
-    assert third[0].track_id != first[0].track_id

tests/test_video.py

@@ -34,7 +34,7 @@ class FakeDetector:
         ]
 
 
-class MovingDetector:
+class TrackedDetector:
     class_names = ["cat"]
 
     def detect(
@@ -56,6 +56,7 @@ class MovingDetector:
                 confidence=0.99,
                 bbox_xyxy=(2.0 + frame_index, 2.0, 12.0 + frame_index, 12.0),
                 bbox_xyxy_norm=(0.1 + offset, 0.1, 0.2 + offset, 0.2),
+                track_id=7,
             )
         ]
 
@@ -121,7 +122,7 @@ def test_process_video_with_fake_detector(tmp_path: Path) -> None:
         capture.release()
 
 
-def test_process_video_assigns_track_ids(tmp_path: Path) -> None:
+def test_process_video_does_not_synthesize_track_ids(tmp_path: Path) -> None:
     video_path = _make_video(tmp_path, fps=10.0, frames=4)
 
     result = process_video(
@@ -129,11 +130,28 @@ def test_process_video_assigns_track_ids(tmp_path: Path) -> None:
         class_prompt="cat",
         frame_stride=1,
         max_frames=3,
-        detector=MovingDetector(),
+        detector=FakeDetector(),
+        image_size=960,
+        max_detections=20,
+        output_dir=str(tmp_path),
+    )
+
+    assert [item.track_id for item in result.detections] == [None, None, None]
+
+
+def test_process_video_preserves_detector_track_ids(tmp_path: Path) -> None:
+    video_path = _make_video(tmp_path, fps=10.0, frames=4)
+
+    result = process_video(
+        video_path=str(video_path),
+        class_prompt="cat",
+        frame_stride=1,
+        max_frames=3,
+        detector=TrackedDetector(),
         output_dir=str(tmp_path),
     )
 
-    assert [item.track_id for item in result.detections] == [1, 1, 1]
+    assert [item.track_id for item in result.detections] == [7, 7, 7]
 
 
 def test_process_video_suppresses_duplicate_same_label_boxes(tmp_path: Path) -> None:

tiny_trigger/__init__.py

@@ -1,7 +1,7 @@
 """Open-vocabulary video automations for Tiny Trigger."""
 
 from .automation import RuleEngine, evaluate_video_detections, load_automation_text
-from .detector import UltralyticsYOLOEDetector, parse_class_prompt
+from .detector import GreedyIoUTracker, UltralyticsYOLOEDetector, parse_class_prompt
 from .llm import (
     DEFAULT_ANTHROPIC_MODEL,
     DEFAULT_OPENAI_MODEL,
@@ -13,6 +13,7 @@ from .llm import (
 from .video import process_video
 
 __all__ = [
+    "GreedyIoUTracker",
     "RuleEngine",
     "UltralyticsYOLOEDetector",
     "DEFAULT_ANTHROPIC_MODEL",

tiny_trigger/automation.py

@@ -3,6 +3,7 @@ from __future__ import annotations
 import json
 from collections import defaultdict
 from math import hypot
+from statistics import median
 from typing import Any, Literal
 
 from pydantic import AliasChoices, BaseModel, ConfigDict, Field, ValidationError, model_validator
@@ -63,7 +64,7 @@ class FarCondition(BaseModel):
 
 class MovingCondition(BaseModel):
     label: str
-    min_displacement_percent: float = Field(default=3.0, ge=0.0)
+    min_displacement_ratio: float = Field(default=0.15, ge=0.0)
     window_frames: int = Field(default=3, ge=3)
     max_missing_frames: int = Field(default=1, ge=0)
 
@@ -240,7 +241,7 @@ class RuleEngine:
         self.rules = rules
         self.last_fired: dict[str, float] = dict(last_fired or {})
         self.last_matched: dict[str, bool] = dict(last_matched or {})
-        self.track_history: dict[int, list[tuple[int, tuple[float, float]]]] = {}
+        self.track_history: dict[int, list[tuple[int, tuple[float, float], float]]] = {}
         self.moving_track_last_seen: dict[tuple[str, int], int] = {}
 
     def evaluate_frame(
@@ -366,13 +367,19 @@ class RuleEngine:
             history = self.track_history.get(detection.track_id, [])
             if len(history) < condition.window_frames:
                 continue
-            _first_frame, first_centroid = history[-condition.window_frames]
-            _last_frame, last_centroid = history[-1]
-            displacement = hypot(
-                last_centroid[0] - first_centroid[0],
-                last_centroid[1] - first_centroid[1],
-            )
-            if displacement >= condition.min_displacement_percent:
+            window = history[-condition.window_frames :]
+            # Smooth out box jitter by averaging each half of the window, then
+            # measure displacement relative to the object's own size (its median
+            # box diagonal) so the threshold is distance-invariant and a flickering
+            # box on a stationary object stays well under it.
+            half = condition.window_frames // 2
+            early = _mean_point([point for _frame, point, _size in window[:half]])
+            late = _mean_point([point for _frame, point, _size in window[-half:]])
+            size = median(size for _frame, _point, size in window)
+            if size <= 0:
+                continue
+            displacement = hypot(late[0] - early[0], late[1] - early[1])
+            if displacement / size >= condition.min_displacement_ratio:
                 self.moving_track_last_seen[(label, detection.track_id)] = detection.frame_index
                 return True
         if label_detections:
@@ -389,7 +396,9 @@ class RuleEngine:
             if detection.track_id is None:
                 continue
             history = self.track_history.setdefault(detection.track_id, [])
-            history.append((detection.frame_index, _centroid_percent(detection)))
+            history.append(
+                (detection.frame_index, _foot_point_percent(detection), _box_diagonal_percent(detection))
+            )
             del history[:-10]
 
     def _mark_cooldowns(self, rule: AutomationRule, timestamp_sec: float) -> None:
@@ -456,9 +465,21 @@ def _box_gap_percent(left: Detection, right: Detection) -> float:
     return max(gap_x, gap_y) * 100.0
 
 
-def _centroid_percent(detection: Detection) -> tuple[float, float]:
+def _foot_point_percent(detection: Detection) -> tuple[float, float]:
+    # Bottom-center ("foot point"): far steadier than the box center for a
+    # standing/grounded object, whose top edge flickers with arms/hair/occlusion.
+    x1, _y1, x2, y2 = detection.bbox_xyxy_norm
+    return ((x1 + x2) * 50.0, y2 * 100.0)
+
+
+def _box_diagonal_percent(detection: Detection) -> float:
     x1, y1, x2, y2 = detection.bbox_xyxy_norm
-    return ((x1 + x2) * 50.0, (y1 + y2) * 50.0)
+    return hypot((x2 - x1) * 100.0, (y2 - y1) * 100.0)
+
+
+def _mean_point(points: list[tuple[float, float]]) -> tuple[float, float]:
+    count = len(points)
+    return (sum(point[0] for point in points) / count, sum(point[1] for point in points) / count)
 
 
 def _trigger_edge(*, previous: bool, matched: bool) -> Literal["enter", "exit", "while", "none"]:

tiny_trigger/detector.py

@@ -39,6 +39,48 @@ class Detector(Protocol):
         ...
 
 
+class GreedyIoUTracker:
+    """Assign stable track IDs to detections by greedy IoU matching across frames.
+
+    Detection stays the source of truth: every detection is returned, matched to an
+    existing track when their same-label boxes overlap enough, or given a fresh ID
+    otherwise. Nothing is dropped for failing to match — unlike a confirm-before-emit
+    tracker (e.g. ByteTrack), which withholds unconfirmed detections and lowers recall.
+    A track that goes unmatched for more than ``max_age`` frames is forgotten.
+    """
+
+    def __init__(self, *, iou_threshold: float = 0.3, max_age: int = 2) -> None:
+        self.iou_threshold = iou_threshold
+        self.max_age = max_age
+        self._tracks: dict[int, dict[str, Any]] = {}
+        self._next_id = 1
+
+    def assign(self, detections: list[Detection]) -> list[Detection]:
+        for track in self._tracks.values():
+            track["age"] += 1
+        assigned: list[Detection] = []
+        used: set[int] = set()
+        for detection in sorted(detections, key=lambda item: item.confidence, reverse=True):
+            label = detection.label.strip().lower()
+            best_id: int | None = None
+            best_iou = self.iou_threshold
+            for track_id, track in self._tracks.items():
+                if track_id in used or track["label"] != label:
+                    continue
+                iou = _box_iou(detection.bbox_xyxy_norm, track["bbox"])
+                if iou >= best_iou:
+                    best_iou = iou
+                    best_id = track_id
+            if best_id is None:
+                best_id = self._next_id
+                self._next_id += 1
+            used.add(best_id)
+            self._tracks[best_id] = {"bbox": detection.bbox_xyxy_norm, "label": label, "age": 0}
+            assigned.append(detection.model_copy(update={"track_id": best_id}))
+        self._tracks = {track_id: track for track_id, track in self._tracks.items() if track["age"] <= self.max_age}
+        return assigned
+
+
 class UltralyticsYOLOEDetector:
     def __init__(
         self,
@@ -46,6 +88,7 @@ class UltralyticsYOLOEDetector:
         class_names: list[str],
         model_name: str = "yoloe-26s-seg.pt",
         device: str | None = None,
+        tracking_enabled: bool = False,
     ) -> None:
         if not class_names:
             raise ValueError("YOLOE needs at least one open-vocabulary class.")
@@ -58,6 +101,8 @@ class UltralyticsYOLOEDetector:
         self.class_names = class_names
         self.model_name = model_name
         self.device = device
+        self.tracking_enabled = tracking_enabled
+        self._tracker = GreedyIoUTracker() if tracking_enabled else None
         self.model = YOLOE(model_name)
         self.model.set_classes(class_names)
 
@@ -82,13 +127,16 @@ class UltralyticsYOLOEDetector:
         results = self.model.predict(frame, **kwargs)
         if not results:
             return []
-        return detections_from_ultralytics_result(
+        detections = detections_from_ultralytics_result(
             results[0],
             frame_shape=frame.shape,
             frame_index=frame_index,
             timestamp_sec=timestamp_sec,
             fallback_names=self.class_names,
         )
+        if self._tracker is not None:
+            detections = self._tracker.assign(detections)
+        return detections
 
 
 def detections_from_ultralytics_result(
@@ -107,10 +155,12 @@ def detections_from_ultralytics_result(
     xyxy_values = boxes.xyxy.cpu().tolist()
     confidences = boxes.conf.cpu().tolist()
     class_ids = boxes.cls.cpu().tolist()
+    raw_track_ids = getattr(boxes, "id", None)
+    track_ids = raw_track_ids.cpu().tolist() if raw_track_ids is not None else [None] * len(xyxy_values)
     names = getattr(result, "names", {}) or {}
 
     detections: list[Detection] = []
-    for bbox, score, class_id in zip(xyxy_values, confidences, class_ids):
+    for bbox, score, class_id, track_id in zip(xyxy_values, confidences, class_ids, track_ids):
         label = _label_from_names(names, int(class_id), fallback_names)
         detections.append(
             Detection(
@@ -120,6 +170,7 @@ def detections_from_ultralytics_result(
                 confidence=float(score),
                 bbox_xyxy=tuple(float(value) for value in bbox),
                 bbox_xyxy_norm=_normalize_box(bbox, width, height),
+                track_id=int(track_id) if track_id is not None else None,
             )
         )
     return suppress_duplicate_detections(detections)
@@ -130,16 +181,32 @@ def suppress_duplicate_detections(
     *,
     iou_threshold: float = 0.8,
 ) -> list[Detection]:
-    """Keep the highest-confidence same-label box for heavily overlapping detections."""
+    """Collapse heavily overlapping same-label boxes into one.
+
+    Keeps the highest-confidence box's geometry/label/score, but carries the
+    *oldest* track_id in the overlap group. Ultralytics assigns track IDs from
+    an incrementing counter, so the smallest ID is the longest-lived track;
+    preferring it keeps identity stable across frames instead of letting it flip
+    with per-frame confidence (which otherwise caused cooldown/count misfires).
+    """
     kept: list[Detection] = []
     for detection in sorted(detections, key=lambda item: item.confidence, reverse=True):
-        duplicate = any(
-            _same_label(detection, existing)
-            and _box_iou(detection.bbox_xyxy_norm, existing.bbox_xyxy_norm) >= iou_threshold
-            for existing in kept
+        match_index = next(
+            (
+                index
+                for index, existing in enumerate(kept)
+                if _same_label(detection, existing)
+                and _box_iou(detection.bbox_xyxy_norm, existing.bbox_xyxy_norm) >= iou_threshold
+            ),
+            None,
         )
-        if not duplicate:
+        if match_index is None:
             kept.append(detection)
+            continue
+        existing = kept[match_index]
+        oldest_id = _oldest_track_id(existing.track_id, detection.track_id)
+        if oldest_id != existing.track_id:
+            kept[match_index] = existing.model_copy(update={"track_id": oldest_id})
     return sorted(kept, key=lambda item: (item.frame_index, item.label, item.bbox_xyxy_norm))
 
 
@@ -173,6 +240,13 @@ def _same_label(left: Detection, right: Detection) -> bool:
     return left.label.strip().lower() == right.label.strip().lower()
 
 
+def _oldest_track_id(left: int | None, right: int | None) -> int | None:
+    ids = [value for value in (left, right) if value is not None]
+    if not ids:
+        return None
+    return min(ids)
+
+
 def _box_iou(
     left: tuple[float, float, float, float],
     right: tuple[float, float, float, float],

tiny_trigger/llm.py

@@ -22,7 +22,7 @@ Use trigger.on="while" only when the user explicitly wants repeated actions whil
 When the request says one object is near, next to, beside, at, by, close to, or in front of another object, you MUST emit a near condition.
 Do not replace a near relation with two present conditions.
 Use max_gap_percent for near/far box-edge distance. It is the largest horizontal/vertical edge gap between boxes in normalized frame percent; touching or overlapping boxes have gap 0.
-Use moving for simple same-object centroid displacement across sampled frames, such as "car moving" or "person walks". Use min_displacement_percent, default 3, window_frames, minimum/default 3, and max_missing_frames, default 1.
+Use moving for simple same-object displacement across sampled frames, such as "car moving" or "person walks". Use min_displacement_ratio, default 0.15 (displacement as a fraction of the object's own box size), window_frames, minimum/default 3, and max_missing_frames, default 1.
 Do not generate speed, direction, long-gap re-identification, or trajectory path rules.
 If the user mentions elapsed time since an action or limiting repeat fires, encode it as gate.cooldown.
 If the user asks for one action when a condition starts and another action when it stops, use trigger.on="change" and then.enter / then.exit.
@@ -470,7 +470,7 @@ JSON:
       "name": "car-moving",
       "when": {{
         "all": [
-          {{"moving": {{"label": "car", "min_displacement_percent": 3, "window_frames": 3, "max_missing_frames": 1}}}}
+          {{"moving": {{"label": "car", "min_displacement_ratio": 0.15, "window_frames": 3, "max_missing_frames": 1}}}}
         ]
       }},
       "trigger": {{"on": "enter"}},

tiny_trigger/tracking.py

@@ -1,85 +0,0 @@
-from __future__ import annotations
-
-from dataclasses import dataclass, field
-from math import hypot
-
-from .models import Detection
-
-
-def _label(value: str) -> str:
-    return value.strip().lower()
-
-
-def _centroid_percent(detection: Detection) -> tuple[float, float]:
-    x1, y1, x2, y2 = detection.bbox_xyxy_norm
-    return ((x1 + x2) * 50.0, (y1 + y2) * 50.0)
-
-
-@dataclass
-class _Track:
-    id: int
-    label: str
-    centroid: tuple[float, float]
-    missed: int = 0
-
-
-class SimpleTracker:
-    """Small label-aware centroid tracker for sampled video detections."""
-
-    def __init__(self, *, max_match_distance_percent: float = 20.0, max_missed: int = 2) -> None:
-        self.max_match_distance_percent = max_match_distance_percent
-        self.max_missed = max_missed
-        self._next_id = 1
-        self._tracks: dict[int, _Track] = {}
-
-    def update(self, detections: list[Detection]) -> list[Detection]:
-        candidates: list[tuple[float, int, int]] = []
-        detection_centroids = [_centroid_percent(detection) for detection in detections]
-        for detection_index, detection in enumerate(detections):
-            label = _label(detection.label)
-            centroid = detection_centroids[detection_index]
-            for track in self._tracks.values():
-                if track.label != label:
-                    continue
-                distance = hypot(centroid[0] - track.centroid[0], centroid[1] - track.centroid[1])
-                if distance <= self.max_match_distance_percent:
-                    candidates.append((distance, detection_index, track.id))
-
-        assigned_detections: set[int] = set()
-        assigned_tracks: set[int] = set()
-        active_tracks: set[int] = set()
-        detection_track_ids: dict[int, int] = {}
-        for _distance, detection_index, track_id in sorted(candidates):
-            if detection_index in assigned_detections or track_id in assigned_tracks:
-                continue
-            assigned_detections.add(detection_index)
-            assigned_tracks.add(track_id)
-            detection_track_ids[detection_index] = track_id
-
-        tracked: list[Detection] = []
-        for detection_index, detection in enumerate(detections):
-            centroid = detection_centroids[detection_index]
-            track_id = detection_track_ids.get(detection_index)
-            if track_id is None:
-                track_id = self._next_id
-                self._next_id += 1
-                self._tracks[track_id] = _Track(
-                    id=track_id,
-                    label=_label(detection.label),
-                    centroid=centroid,
-                )
-            else:
-                track = self._tracks[track_id]
-                track.centroid = centroid
-                track.missed = 0
-            active_tracks.add(track_id)
-            tracked.append(detection.model_copy(update={"track_id": track_id}))
-
-        for track_id, track in list(self._tracks.items()):
-            if track_id in active_tracks:
-                continue
-            track.missed += 1
-            if track.missed > self.max_missed:
-                del self._tracks[track_id]
-
-        return tracked

tiny_trigger/video.py

@@ -1,20 +1,22 @@
 from __future__ import annotations
 
 import hashlib
+import logging
 import shutil
 import subprocess
 import tempfile
+import time
 from pathlib import Path
 from typing import Callable
 from uuid import uuid4
 
 from .detector import Detector, UltralyticsYOLOEDetector, parse_class_prompt, suppress_duplicate_detections
 from .models import ActionEvent, Detection, FrameSample, VideoProcessResult
-from .tracking import SimpleTracker
 
 
 ProgressCallback = Callable[[int, int | None], None]
 MP4_CODEC = "mp4v"
+LOGGER = logging.getLogger(__name__)
 
 
 def process_video(
@@ -29,6 +31,7 @@ def process_video(
     image_size: int | None = None,
     device: str | None = None,
     max_detections: int | None = None,
+    tracking_enabled: bool = False,
     detector: Detector | None = None,
     output_dir: str | None = None,
     progress: ProgressCallback | None = None,
@@ -53,7 +56,22 @@ def process_video(
     if max_detections is not None and max_detections < 1:
         raise ValueError("max_detections must be at least 1.")
 
-    detector = detector or UltralyticsYOLOEDetector(class_names=classes, model_name=model_name, device=device or None)
+    if detector is None:
+        LOGGER.info(
+            "Loading detector model=%s classes=%s device=%s tracking=%s",
+            model_name,
+            ", ".join(classes),
+            device or "auto",
+            tracking_enabled,
+        )
+        detector_started = time.perf_counter()
+        detector = UltralyticsYOLOEDetector(
+            class_names=classes,
+            model_name=model_name,
+            device=device or None,
+            tracking_enabled=tracking_enabled,
+        )
+        LOGGER.info("Detector loaded in %.2fs", time.perf_counter() - detector_started)
     capture = cv2.VideoCapture(video_path)
     if not capture.isOpened():
         raise ValueError(f"Could not open video: {video_path}")
@@ -76,10 +94,18 @@ def process_video(
 
     detections: list[Detection] = []
     frames: list[FrameSample] = []
-    tracker = SimpleTracker()
     processed_frames = 0
     frame_index = -1
     latest_detections: list[Detection] = []
+    LOGGER.info(
+        "Processing video=%s fps=%.2f size=%sx%s sample_stride=%s max_frames=%s",
+        video_path,
+        source_fps,
+        width,
+        height,
+        effective_frame_stride,
+        max_frames,
+    )
     try:
         while True:
             ok, frame = capture.read()
@@ -91,8 +117,16 @@ def process_video(
                     break
                 timestamp_sec = frame_index / source_fps
                 frames.append(FrameSample(frame_index=frame_index, timestamp_sec=timestamp_sec))
+                LOGGER.info(
+                    "Detecting sampled frame %s/%s source_frame=%s timestamp=%.2fs",
+                    processed_frames + 1,
+                    max_frames,
+                    frame_index,
+                    timestamp_sec,
+                )
+                detect_started = time.perf_counter()
                 frame_detections = detector.detect(
-                    frame,
+                    frame.copy(),
                     frame_index=frame_index,
                     timestamp_sec=timestamp_sec,
                     confidence=confidence,
@@ -100,9 +134,18 @@ def process_video(
                     max_detections=max_detections,
                 )
                 frame_detections = suppress_duplicate_detections(frame_detections)
-                latest_detections = tracker.update(frame_detections)
+                latest_detections = frame_detections
                 detections.extend(latest_detections)
                 processed_frames += 1
+                tracked_count = sum(1 for detection in latest_detections if detection.track_id is not None)
+                LOGGER.info(
+                    "Detected sampled frame %s/%s in %.2fs: detections=%s tracked=%s",
+                    processed_frames,
+                    max_frames,
+                    time.perf_counter() - detect_started,
+                    len(latest_detections),
+                    tracked_count,
+                )
                 if progress:
                     progress(processed_frames, max_frames)
 
@@ -111,7 +154,14 @@ def process_video(
     finally:
         writer.release()
         capture.release()
+    LOGGER.info("Finalizing annotated video %s", output_path)
     _finalize_browser_mp4(output_path)
+    LOGGER.info(
+        "Finished video processing: sampled_frames=%s detections=%s output=%s",
+        processed_frames,
+        len(detections),
+        output_path,
+    )
 
     return VideoProcessResult(
         output_video_path=str(output_path),
@@ -175,6 +225,13 @@ def render_automation_video(
     frame_index = -1
     latest_detections: list[Detection] = []
     latest_events: list[ActionEvent] = []
+    LOGGER.info(
+        "Rendering automation video=%s fps=%.2f sample_stride=%s max_frames=%s",
+        source_video_path,
+        source_fps,
+        effective_frame_stride,
+        max_frames,
+    )
     try:
         while True:
             ok, frame = capture.read()
@@ -195,7 +252,9 @@ def render_automation_video(
     finally:
         writer.release()
         capture.release()
+    LOGGER.info("Finalizing automation video %s", output_path)
     _finalize_browser_mp4(output_path)
+    LOGGER.info("Finished automation render: output=%s", output_path)
 
     return str(output_path)