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----
-license: apache-2.0
-language:
-  - zh
-  - en
-tags:
-  - traffic
-  - perception
-  - object-detection
-  - tracking
-  - intersection
-  - yolo
-  - yolo11
-  - deepsort
-  - computer-vision
-  - autonomous-driving
-  - v1
-pipeline_tag: object-detection
-library_name: custom
-base_model: Ultralytics/yolo11
----
-<div align="center">
-  <img src="https://huggingface.co/datasets/OpenTraffic/opentraffic-perception-engine/resolve/main/opentraffic-perception-engine-main/figure/LOGO2x.png" width="70%" />
-[![Paper](https://img.shields.io/badge/Paper-A42C25?style=for-the-badge&logo=arxiv&logoColor=white)](https://arxiv.org/abs/XXXX.XXXXX)
-[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg?style=for-the-badge)](LICENSE)
-[![Python](https://img.shields.io/badge/Python-3.13+-green.svg?style=for-the-badge&logo=python&logoColor=white)](https://www.python.org/)
-[![Platform](https://img.shields.io/badge/Platform-Linux%20x86__64-orange.svg?style=for-the-badge&logo=linux&logoColor=white)]()
-<br/>
-[![Hugging Face](https://img.shields.io/badge/🤗%20HuggingFace-OpenTraffic-yellow.svg?style=for-the-badge)](https://huggingface.co/OpenTraffic-Team)
-[![GitHub](https://img.shields.io/badge/GitHub-181717?style=for-the-badge&logo=github&logoColor=white)](https://github.com/OpenTraffic-Team/Tir)
-[![WeChat](https://img.shields.io/badge/WeChat%20Group-07C160?style=for-the-badge&logo=wechat&logoColor=white)](https://github.com/OpenTraffic-Team/Tir/issues/1)
-</div>
-## TIR — Open-source traffic perception engine for urban intersections
-Supports multi-stream RTSP input, YOLO11 + DeepSort detection and tracking, homography-based coordinate transformation, world velocity estimation, lane-level structured output, and radar-reference validation. Designed for Perception-Driven TSC scenarios.
-<div align="center">
-  <img src="https://huggingface.co/datasets/OpenTraffic/opentraffic-perception-engine/resolve/main/opentraffic-perception-engine-main/figure/Framework.png" width="90%">
-</div>
-<br/>
-> **Paper:** https://arxiv.org/abs/XXXX.XXXXX
-> **Project Page:** https://opentraffic-team.github.io/tir
-> **HuggingFace:** https://huggingface.co/OpenTraffic-Team
-> **GitHub:** https://github.com/OpenTraffic-Team/Tir
-<br/>
-## 🌟 Table of Contents
-- [🎬 Demo Video](#-demo-video)
-- [📢 News](#-news)
-- [📋 TODO List](#-todo-list)
-- [🚀 Quick Start](#-quick-start)
-  - [Environment Requirements](#environment-requirements)
-  - [Installation](#installation)
-  - [Start Redis](#start-redis)
-  - [Prepare Video Streams](#prepare-video-streams)
-  - [Start the System](#start-the-system)
-- [✏️ Configuration](#️-configuration)
-  - [Camera Fields](#camera-fields)
-  - [Global Fields](#global-fields)
-  - [Example](#example)
-- [✨ Features](#-features)
-- [📦 .so Delivery Version](#-so-delivery-version)
-- [🗄️ Redis Data Format](#️-redis-data-format)
-- [📊 Performance](#-performance)
-- [📖 Paper](#-paper)
-- [🤗 Hugging Face](#-hugging-face)
-- [🌐 Social Media](#-social-media)
-- [🔖 Citation](#-citation)
-- [📄 License](#-license)
-<br/>
-## 🎬 Demo Video
-<div align="center">
-  <img src="https://huggingface.co/datasets/OpenTraffic/opentraffic-perception-engine/resolve/main/opentraffic-perception-engine-main/figure/video_cover.png" width="80%">
-</div>
-<br/>
-## 📢 News
-- **[2026/05/27]** Restructured README to align with unified English homepage style.
-- **[2026/05/15]** Initial release of TIR, supporting real-time multi-direction intersection perception.
-<br/>
-## 📋 TODO List
-- [x] YOLO11 + DeepSort detection and tracking pipeline
-- [x] Four-direction (E/S/W/N) data fusion
-- [x] Homography matrix pixel → radar world coordinate transformation
-- [x] Real-time Redis Stream writing
-- [x] Hungarian algorithm cross-modal ID matching (vision ↔ radar)
-- [x] In-frame OCR timestamp extraction
-- [x] Lane-level structured output
-- [x] Radar-reference validation pipeline
-- [ ] Release pretrained model weights to HuggingFace
-- [ ] Support more intersection configurations
-- [ ] Publish technical report / ArXiv paper
-- [ ] Web visualization dashboard
-- [ ] Support more camera protocols
-<br/>
-## 🚀 Quick Start
-### Environment Requirements
-| Item | Requirement |
-|---|---|
-| OS | Linux x86_64 |
-| NVIDIA Driver | ≥ 560 |
-| CUDA | 12.6 |
-| Python | 3.13+ |
-| PyTorch | 2.11.0+cu126 |
-### Installation
-```bash
-# Clone the repository
-git clone https://github.com/OpenTraffic-Team/opentraffic-perception-engine.git
-cd opentraffic-perception-engine-main/opentraffic-TIR
-# Install dependencies
-pip install -r requirements.txt
-```
-### Start Redis
-```bash
-redis-server redis.conf
-```
-### Prepare Video Streams
-**Use mediamtx to serve local video as RTSP:**
-```bash
-./mediamtx mediamtx.yml
-```
-**Or use VLC to stream:**
-```bash
-vlc video.mp4 --sout '#rtp{sdp=rtsp://:8554/test}'
-```
-**Or use a local video path directly:** Set the `rtsp_url` field in `drivers/config.json` to a local file path (e.g. `input/45_0429.mp4`).
-> Test video: https://pan.baidu.com/s/1qULF2WcxUP_l5Cvs9uV-JA  Password: 1234
-### Start the System
-```bash
-./run_local.sh
-```
-Stop:
-```bash
-./stop_local.sh
-```
-<br/>
-## ✏️ Configuration
-Edit `opentraffic-TIR/drivers/config.json`.
-### Camera Fields
-| Field | Type | Description |
-|---|---|---|
-| `id` | string | Camera ID, format: `{intersection_id}_{direction}` |
-| `rtsp_url` | string | RTSP URL or local video file path |
-| `window` | `[x1, y1, x2, y2]` | Detection crop window; use `[0, 0, 1920, 1080]` for full frame |
-| `H` | 3×3 matrix | Pixel → radar homography matrix |
-| `H_inv` | 3×3 matrix | Radar → pixel inverse matrix (auto-computed from H if omitted) |
-| `radar_variant` | string | Coordinate variant, default `"default"` |
-| `radar_redis_key` | string | Redis key for this camera's output |
-### Global Fields
-| Field | Description |
-|---|---|
-| `debugMode` | Enable verbose tracking logs |
-| `jsonlOutputDir` | JSONL output directory |
-| `radarReferenceJsonl` | Radar reference file for validation |
-| `matchTargetDir` | Radar JSON output directory for cross-modal ID matching |
-| `matchTimeMaxDeltaMs` | Max timestamp matching tolerance (ms) |
-| `vehicleMatchMaxDist` | Max vehicle center matching distance (meters) |
-| `localRedisConfig.host` | Redis server address |
-| `localRedisConfig.password` | Redis password |
-| `data_processing.upload_interval_sec` | JSON merge and upload interval (seconds) |
-### Example
-```json
-{
-  "intersection": {
-    "id": "HHL_QHDD",
-    "name": "Honghua Road",
-    "cameras": [
-      {
-        "id": "HHL_QHDD_S",
-        "rtsp_url": "./input/45_0429.mp4",
-        "window": [0, 0, 1920, 1080],
-        "H": [[...], [...], [...]],
-        "H_inv": [[...], [...], [...]],
-        "radar_variant": "default",
-        "radar_redis_key": "origin_info_state:HHL_QHDD"
-      }
-    ]
-  },
-  "debugMode": false,
-  "jsonlOutputDir": "./control_group_fullspeed_jsonl",
-  "radarReferenceJsonl": "./reference_radar/HHL_QHDD_S/HHL_QHDD_S_shard_00000.jsonl",
-  "vehicleMatchMaxDist": 10.0,
-  "localRedisConfig": {
-    "host": "127.0.0.1",
-    "port": 6379,
-    "db": 0,
-    "password": "your_password"
-  },
-  "data_processing": {
-    "upload_interval_sec": 1
-  }
-}
-```
-<br/>
-## ✨ Features
-### 🚗 Vehicle Detection
-- Vehicle detection and multi-object tracking (YOLO11 + DeepSort) for intersection video.
-- Compatible with RTSP streams and local video files.
-- Recognition engine compiled to `.so` for acceleration; outputs structured target IDs for downstream use.
-### 🗺️ World Coordinate Output
-- Projects image-space targets into world coordinates.
-- Uses calibrated homography matrix (H / H_inv) for pixel-to-world mapping.
-- Outputs radar-style coordinates for downstream integration.
-### 💨 World Velocity Estimation
-- Kalman-filter-based `SimpleVelocityEstimator` estimates target velocity in world coordinates.
-- Outputs structured velocity fields (m/s) per tracked vehicle.
-- Supports traffic state interpretation and speed comparison against radar reference.
-### 🛣️ Lane-Level Output
-- Associates detected targets with lane semantics.
-- Generates lane-level structured results for consumption by control systems.
-### 🕐 Timestamp Extraction
-- OCR extracts timestamps from video frames for accurate temporal alignment.
-### 🔗 Cross-Modal ID Matching
-- Hungarian algorithm aligns visual targets with external radar data by ID.
-<br/>
-## 📦 .so Delivery Version
-This section applies to the self-contained `.so` delivery package, which runs without the full source code.
-### Included Resources
-| Resource | Path |
-|---|---|
-| Input video | `input/45_0429.mp4` |
-| Radar reference data | `reference_radar/HHL_QHDD_S/HHL_QHDD_S_shard_00000.jsonl` |
-| Python environment | `.venv` |
-| Start script | `run_local.sh` |
-| Stop script | `stop_local.sh` |
-### Running
-Start:
-```bash
-cd /tir-0513-git-so
-./run_local.sh
-```
-Stop:
-```bash
-cd /tir-0513-git-so
-./stop_local.sh
-```
-### Output Format
-Default output directory:
-```
-control_group_fullspeed_jsonl/HHL_QHDD_S/HHL_QHDD_S_shard_00000.jsonl
-```
-Output JSONL fields:
-| Field | Description |
-|---|---|
-| `timestamp_ms` | Frame timestamp (milliseconds) |
-| `intersection_id` | Intersection ID |
-| `camera_id` | Camera ID |
-| `source` | Data source |
-| `coordinate_space` | Coordinate space |
-| `vehicles[].id` | Vehicle ID |
-| `vehicles[].center` | Vehicle center coordinates (meters) |
-| `vehicles[].speed` | Velocity vector (m/s) |
-| `vehicles[].speed_scalar` | Speed scalar (m/s) |
-| `vehicles[].lane` | Lane |
-| `vehicles[].type` | Vehicle type |
-<br/>
-## 🗄️ Redis Data Format
-Results are written to two Redis keys:
-- **Snapshot**: `recognition_{camera_id}_snap`
-- **Stream**: `recognition_{camera_id}`
-Merged four-direction snapshot format (`origin_info_state:{intersection_id}`):
-```json
-{
-  "code": "HHL_QHDD",
-  "name": "Honghua Road",
-  "recognitionSnap[HHL_QHDD_E]": {
-    "timestamp": 1734400000.0,
-    "vehicles": [
-      {
-        "id": "42",
-        "orig_id": "7",
-        "type": "",
-        "lane": "",
-        "center": [12.5, 8.3],
-        "speed": [3.2, -1.1],
-        "licenseNum": ""
-      }
-    ]
-  }
-}
-```
-| Field | Description |
-|---|---|
-| `timestamp` | Frame timestamp in seconds (extracted via OCR) |
-| `id` | Target ID after radar matching |
-| `orig_id` | Original visual tracking ID (before matching) |
-| `center` | World coordinates `[x, y]` in meters |
-| `speed` | Velocity components `[vx, vy]` in m/s |
-| `type` | Vehicle type (if available) |
-| `licenseNum` | License plate number (if available) |
-<br/>
-## 📊 Performance
-> Evaluation: historical full-set speed JSONL vs. raw radar JSONL.
-### Field Error Comparison
-| Field | Mean | Median (p50) | p75 | p90 | Notes |
-|---|---|---|---|---|---|
-| `timestamp_ms` error (ms) | 25.04 | 26.5 | 37 | 46 | Time delta between video frame and nearest radar frame |
-| `vehicles[].center` error (m) | 2.5132 | 1.6251 | 3.1987 | 6.7021 | Spatial position error of matched targets |
-| `vehicles[].speed_scalar` error (m/s) | 1.6727 | 0.3176 | 1.7741 | 5.0061 | Speed scalar error of matched targets |
-### Field Consistency
-| Field | Comparison | Consistency |
-|---|---|---|
-| `intersection_id` | Exact match | **100%** |
-| `camera_id` | Exact match | **100%** |
-| `coordinate_space` | Exact match | **100%** |
-| `timestamp_ms` | Aligned within 200ms | **100%** |
-| `vehicles[].lane` | Exact match with radar | **100%** |
-<br/>
-## 📖 Paper
-- Title: `OpenTraffic Perception System for Perception-Driven TSC`
-- Status: `In preparation / pending release`
-- Placeholder: `https://arxiv.org/abs/XXXX.XXXXX`
-<br/>
-## 🤗 Hugging Face
-- Model page: `https://huggingface.co/OpenTraffic-Team`
-- Planned releases:
-  - Packaged perception model
-  - Sample output results
-  - Benchmark examples
-<br/>
-## 🌐 Social Media
-- GitHub: `https://github.com/OpenTraffic-Team/Tir`
-- WeChat Group: [Join Discussion](https://github.com/OpenTraffic-Team/Tir/issues/1)
-- X / Twitter: `Coming soon`
-<br/>
-## 🔖 Citation
-If TIR is useful for your research, please cite:
-```bibtex
-@article{tir2026,
-  title   = {TIR: Real-Time Traffic Situation Awareness for Urban Intersections},
-  author  = {OpenTraffic Team},
-  journal = {arXiv preprint},
-  year    = {2026}
-}
-```
-<br/>
-## 📄 License
-This project is released under the [Apache 2.0 License](LICENSE).
----
-<div align="center">
-  <b>OpenTraffic Team</b> | Making urban traffic smarter.
-</div>