| # DynaFall experiments |
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| This repository implements the experiment plan in `PLAN.md` for skeleton-based fall detection. |
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|
| ## Structure |
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| ```text |
| data/raw/ Raw videos: data/raw/URFD and data/raw/MCFD |
| data/poses/ Extracted YOLO pose files |
| data/processed/ Video-level splits and 32-frame clips |
| configs/default.yaml Main experiment config |
| src/dynafall/ Dataset, features, models, training, evaluation |
| scripts/ Entry-point scripts |
| results/ Metrics, checkpoints, tables |
| ``` |
|
|
| ## Quick smoke test |
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| ```bash |
| python scripts/make_synthetic_dataset.py --dataset Synthetic --videos 24 |
| python scripts/prepare_clips.py --dataset Synthetic |
| python scripts/train.py --dataset Synthetic --method dynafall --epochs 2 |
| python scripts/evaluate.py --dataset Synthetic --method dynafall |
| ``` |
|
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| ## Real data workflow |
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| Place videos under: |
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| ```text |
| data/raw/URFD/fall/*.avi |
| data/raw/URFD/nonfall/*.avi |
| data/raw/MCFD/fall/*.avi |
| data/raw/MCFD/nonfall/*.avi |
| ``` |
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| Any common video extension is accepted. Labels are inferred from the parent directory name: |
| `fall`, `falls`, `1`, `positive` map to fall; all other directory names map to non-fall. |
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| Then run: |
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| ```bash |
| python scripts/extract_pose.py --dataset URFD |
| python scripts/prepare_clips.py --dataset URFD |
| python scripts/run_experiments.py --dataset URFD --methods lstm stgcn agcn ctrgcn posec3d tcnte dynafall |
| python scripts/robustness.py --dataset URFD --methods stgcn agcn ctrgcn posec3d tcnte dynafall |
| python scripts/aggregate_results.py |
| ``` |
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| Repeat for `MCFD`. Cross-dataset evaluation: |
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| ```bash |
| python scripts/evaluate.py --dataset MCFD --method dynafall --checkpoint results/URFD/dynafall/best.pt --tag trainURFD_testMCFD |
| ``` |
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| ## Notes |
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| The graph baselines are compact reimplementations designed for small fall datasets and a COCO-17 pose layout. They preserve the paper-level comparison categories: LSTM, ST-GCN-style graph temporal model, two-stream adaptive GCN, CTR-GCN-style channel topology refinement, PoseC3D-style heatmap volume, TCN+Transformer, and DynaFall-GCN. |
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