--- license: mit task_categories: - video-classification tags: - thermal - action-recognition - human-action-recognition pretty_name: ota size_categories: - 1K ## Structure ``` ├── train/ │ ├── session_1/ # Session 1: Single-person activities │ │ ├── sub_1/ │ │ │ └── thermal/ │ │ │ ├── 1_1_0/ # Action clips as image folders │ │ │ ├── 1_2_0/ │ │ │ └── ... │ │ ├── sub_2/ │ │ └── ... │ └── session_2/ # Session 2: Two-person interactions │ ├── exp_1/ │ │ └── thermal/ │ │ ├── 1/ │ │ ├── 2/ │ │ └── ... │ └── ... ├── val/ │ ├── session_1/ │ └── session_2/ ├── test/ │ ├── test_s1/ # Session 1: Single-person activities │ │ ├── sub_4/ │ │ │ └── thermal/ │ │ │ ├── 1_1/ # Action clips as image folders │ │ │ ├── 1_2/ │ │ │ └── ... │ │ ├── sub_7/ │ │ ├── sub_14/ │ │ └── ... │ └── test_s2/ # Session 2: Two-person interactions │ ├── exp_7/ │ │ └── thermal/ │ │ ├── 1/ │ │ ├── 2/ │ │ └── ... │ └── ... ├── test_wild/ │ ├── test_s1/ # Session 1: Single-person activities │ │ ├── sub_1/ │ │ │ └── thermal/ │ │ │ ├── 1_1/ # Action clips as image folders │ │ │ ├── 1_2/ │ │ │ └── ... │ │ ├── sub_2/ │ │ ├── sub_3/ │ │ └── ... │ └── test_s2/ # Session 2: Two-person interactions │ ├── exp_1/ │ │ └── thermal/ │ │ ├── 1/ │ │ ├── 2/ │ │ └── ... │ └── ... ├── pyskl_annotations ├── annotations_train.txt ├── annotations_val.txt ├── annotations_test.txt └── annotations_wild_test.txt ``` ## Archived Files For easier download, each subject/experiment is archived separately: **Session 1 (Single-Person Actions):** - `sub_1.tar.gz` - `sub_2.tar.gz` - `sub_3.tar.gz` - ... (one archive per subject) **Session 2 (Multi-Person Actions):** - `exp_1.tar.gz` - `exp_2.tar.gz` - ... (one archive per experiment) ## Annotation Format Each line in `annotations_train.txt`, `annotations_val.txt`, and `annotations_test.txt` contains: ``` path_to_sequence action_class ``` Example: ``` test_s1/sub_4/thermal/1_1 0 ``` action_class (0-14): Sports activities action_class (15-27): Daily activities ## Skeleton Annotations in the PYSKL-format The dataset was annotated with bounding boxes and 17-keypoints in the COCO format. We arranged skeletons in the PYSKL format to train using the [PYSKL](https://github.com/kennymckormick/pyskl/blob/main/configs/rgbpose_conv3d/README.md) library. **Structure** ``` pyskl_annotations/ ├── pyskl_ota.pkl (full dataset) ├── pyskl_ota_75.pkl (75% of frames) ├── pyskl_ota_50.pkl (50% of frames) ├── pyskl_ota_25.pkl (25% of frames) ├── pyskl_ota_session1.pkl (full dataset, session 1 only) ├── pyskl_ota_session2.pkl (full dataset, session 2 only) ├── pyskl_ota_75_session1.pkl (75% frames, session 1) ├── pyskl_ota_75_session2.pkl (75% frames, session 2) ├── pyskl_ota_50_session1.pkl (50% frames, session 1) ├── pyskl_ota_50_session2.pkl (50% frames, session 2) ├── pyskl_ota_25_session1.pkl (25% frames, session 1) ├── pyskl_ota_25_session2.pkl (25% frames, session 2) ├── ... ``` **Full Dataset** - pyskl_ota.pkl: Full dataset with all frames (train/val from Balcony, test from Office) - pyskl_ota_session1.pkl: Full dataset, session 1 only (train/val: session_1, test: test_s1) - pyskl_ota_session2.pkl: Full dataset, session 2 only (train/val: session_2, test: test_s2) **Frame-Reduced (75%)** - **pyskl_ota_75.pkl**: Frame-reduced to first 75% of frames - **pyskl_ota_75_session1.pkl**: 75% frames, session 1 only - **pyskl_ota_75_session2.pkl**: 75% frames, session 2 only **Frame-Reduced (50%)** - pyskl_ota_50.pkl: Frame-reduced to first 50% of frames - pyskl_ota_50_session1.pkl: 50% frames, session 1 only - pyskl_ota_50_session2.pkl: 50% frames, session 2 only **Frame-Reduced (25%)** - pyskl_ota_25.pkl: Frame-reduced to first 25% of frames - pyskl_ota_25_session1.pkl: 25% frames, session 1 only - pyskl_ota_25_session2.pkl: 25% frames, session 2 only ## Github link: https://github.com/IS2AI/OpenThermalAction ## Models link: https://huggingface.co/issai/thermal-skeleton-models