Datasets:
| license: cc-by-4.0 | |
| task_categories: | |
| - video-classification | |
| - object-detection | |
| tags: | |
| - surveillance | |
| - fall-detection | |
| - sentinel | |
| - video-intelligence | |
| # Le2i Sentinel Test Frames | |
| Extracted frames from the Le2i Fall Detection Dataset for benchmarking the Sentinel video intelligence pipeline. | |
| ## Contents | |
| - 130 annotated videos (99 falls, 31 normals) | |
| - 3 frames per video (390 total) | |
| - 4 environments: Coffee_room_01, Coffee_room_02, Home_01, Home_02 | |
| - Ground truth in metadata/ground_truth.json | |
| ## Source | |
| Le2i Fall Detection Dataset (University of Burgundy) | |
| - Resolution: 320x240 @ 25fps | |
| - Citation: Charfi et al., "Optimised spatio-temporal descriptors for real-time fall detection", JEI 2013 | |
| ## Usage | |
| ```python | |
| from huggingface_hub import snapshot_download | |
| snapshot_download("PixelML/le2i-sentinel-frames", local_dir="le2i_frames") | |
| ``` | |
| ## Benchmark Results (Sentinel V03 on Mistral Small 3.2) | |
| - Fall detection: F1=0.889, P=0.988, FPR=0.032 | |
| - Cosmos-Embed1 alert gate: 9/10 correct | |