liguo12/Tencent_Fall_Detection

Tencent Fall Detection: test videos and reference results

Six 1 fps test videos from two rooms, with the complete output of the fall-detection and daily-report system for each. Only days 6 and 11 contain a fall; the other days have none.

Video	Duration	Report generation	Room	Report date	What it shows
day_1_1fps.mp4	2 h 47 min	11 min	1	2026-08-06	Resident A (the resident) and Resident B (household member) with visitors
day_6_1fps.mp4	3 h 30 min	11 min	1	2026-08-07	a fall by Resident A; two visitors
day_11_1fps.mp4	8 h 12 min	17 min	1	2026-08-08	a fall by Resident A; one brief visitor
day_7_1fps.mp4	8 h 48 min	25 min	1	2026-08-09	Resident A with visitors
day_8_1fps.mp4	8 h 03 min	20 min	1	2026-08-10	Resident A with visitors
day_19_1fps.mp4	7 h 46 min	24 min	2	2026-08-11	the face-recognition fallback: nobody enrolled for face recognition appears, so the person present longest becomes the resident, shown as "the resident" (not "Resident A")

Report generation is the VLM segment summaries plus the language-model summary and report, model loading included, on one NVIDIA RTX 5090. Face recognition runs alongside on the CPU and does not lengthen it.

results/<video name>/json_summary/daily_report.json is the system's final output (overview, timeline, narratives, routine comparison, verified falls). The rest is supporting evidence: verify/ (fall-alarm verdicts and clips), stgcn/ (detector overlay videos and the presence CSV) and the other json_summary/ files (per-segment observations and the language model's input). Face-recognition data is not published. Each room-1 report compares its routine with the earlier dates (day 1, the first, has none); day 19 (room 2) has no baseline.

The reports were regenerated on 2026-10-01 with the current report code (GitHub feature/identity-linker, commit d4d52a1: fall times read on the right clock, visit edges fitted to the detector, the get-up time after a fall, company the detector does not show removed, and other fixes). Only the language-model summary and report were re-run: the detector output, the fall-alarm verdicts (re-run: the same answers), the clips and the VLM segment summaries are the earlier run's, except that verify/verdicts.json now gives each confirmed fall's time on the video's own clock (fall_time_absolute). The report-generation times above are the earlier full runs'.

Alternative models: Nemotron throughout

results_nemotron/<video name>/ holds the same six days with Nemotron 3 Nano Omni 30B-A3B Reasoning (NVFP4) in place of every Qwen model: fall verifier, segment-summary VLM and the language model of the summary and report. Made with GitHub branch feature/vlm-variants (commit 1ab233f), the stages VERIFIER=nemotron MAP_VLM=nemotron SUMMARY_LLM=nemotron bash bash/run_video_system.sh runs, on the same detector output, alarm events and face-recognition data as results/. Each folder has json_summary/ (the summary, the report and the VLM's per-segment answers), verify/ (the alarm events and Nemotron's verdicts) and action_classes.json; detector files and clips are those of results/.

Six days	Qwen (results/)	Nemotron (results_nemotron/)
Real falls confirmed (of 2) / false falls	2 / 0	2 / 0
Real visitors found (of 14) / extra people listed	14 / 0	14 / 4
Resident's time in the room, mean error per day	3.1 %	5.8 %
Resident's glasses described right (days)	6 / 6	4 / 6
Better daily summary in a blind comparison (days)	3 / 6	3 / 6

Nemotron describes one person's clothes, glasses or hair differently from one 10-minute segment to the next, so a visitor can be listed twice (days 1, 6, 19) and part of the resident's day can appear as a visitor (day 8). Its report calls the woman resident of day 19 "he". Qwen remains the default.