| # UBnormal dataset | [COSKAD](https://github.com/aleflabo/COSKAD) | [Original Repository](https://github.com/lilygeorgescu/UBnormal) |
| --- |
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| Skeletal version proposed in |
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| **[Contracting Skeletal Kinematics for Human-Related Video Anomaly Detection](https://arxiv.org/abs/2301.09489)**</br> |
| *Alessandro Flaborea\*, Guido D'Amely\*, Stefano D'Arrigo\*, Marco Aurelio Sterpa, Alessio Sampieri, Fabio Galasso* |
| --- |
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| We propose HR-UBnormal as an extension of the original UBnormal dataset [\[1\]]((#references)) with kinematic motion representations and a selected set of anomalies that relate only to human behaviors. |
| First, AlphaPose [\[2\]](#references) was used to extract the poses, and PoseFlow [\[3\]](#references) was used to track the skeletons throughout each video. |
| Then, we filtered out the non-human related anomalies. We removed the sub-sequences in which the only anomalous object was not a person (e.g., a car) or the anomaly cannot be detected using only body poses (e.g., fire in the scene). |
| As a result, we left the validation set unaltered while eliminating the frames 2.32% of the test set. |
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| ## Notes regarding the file names' format |
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| The UBnormal dataset annotated with the skeletal representation and its Human-Related version (HR) are released with the following directory structure: |
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| ``` |
| UBnormal |
| | |
| |__________ hr_bool_masks |
| | | |
| | |__________ testing |
| | | | |
| | | |__________ test_frame_mask |
| | | |_______________{scene_id}_{clip_id}.npy |
| | | |_______________... |
| | | |_______________{scene_id}_{clip_id}.npy |
| | | |
| | |__________ validating |
| | | |
| | |__________ test_frame_mask |
| | |_______________{scene_id}_{clip_id}.npy |
| | |_______________... |
| | |_______________{scene_id}_{clip_id}.npy |
| | |
| |__________ training |
| | | |
| | |__________ trajectories |
| | | |
| | |_________{scene_id}_{clip_id} |
| | | |
| | |_________00001.csv |
| | |_________... |
| | |_________0000{n}.csv |
| | |
| |__________ testing |
| | | |
| | |__________ trajectories |
| | | | |
| | | |_________{scene_id}_{clip_id} |
| | | | |
| | | |_________00001.csv |
| | | |_________... |
| | | |_________0000{n}.csv |
| | | |
| | |__________ test_frame_mask |
| | | |
| | |_______________{scene_id}_{clip_id}.npy |
| | |_______________... |
| | |_______________{scene_id}_{clip_id}.npy |
| | |
| |__________ validating |
| | |
| |__________ trajectories |
| | | |
| | |_________{scene_id}_{clip_id} |
| | | |
| | |_________00001.csv |
| | |_________... |
| | |_________0000{n}.csv |
| | |
| |__________ test_frame_mask |
| | |
| |_______________{scene_id}_{clip_id}.npy |
| |_______________... |
| |_______________{scene_id}_{clip_id}.npy |
| ``` |
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| In the `hr_bool_masks`, the frames which were anomalous in the original version but where the anomaly didn't involve any human being are toggled to 'normal', i.e., they are toggled from 1 to 0. |
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| Regarding the naming of the files, since our code expects the `scene_id` and the `clip_id` to be integers and some of the file names in the original dataset were overloaded, the following mapping has been adopted: |
|
|
| - |
| ``` |
| scene_id = {c1c2c3} |
| ``` |
|
|
| where `c1` is the scene type (`{'abnormal':0, 'normal':1}`) and `c2c3` is the scene number of the corresponding file in the original dataset. |
|
|
| - |
| ``` |
| clip_id = {c1c2c3c4} |
| ``` |
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|
| where `c1c2` is the scenario number (i.e., clip id) of the corresponding file in the original dataset, `c3c4` is the remaining id part dubbed as version. Indeed, in the original dataset some videos have names as in the following example: |
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| - `normal_scene_1_scenario1_1` |
| - `normal_scene_1_scenario1_10` |
| - `abnormal_scene_9_scenario_1_fog` |
| - `abnormal_scene_12_scenario_1_smoke` |
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| In order to keep the information regarding the environment in the clip (e.g., fog, smoke, ...), this mapping has been adopted: |
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| ``` |
| {'fog': 51, 'fire': 52, 'smoke': 53} |
| ``` |
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| ## Citation |
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| If you find this dataset useful in your research, please consider cite: |
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| ``` |
| @misc{flaborea2023contracting, |
| title={Contracting Skeletal Kinematics for Human-Related Video Anomaly Detection}, |
| author={Alessandro Flaborea and Guido D'Amely and Stefano D'Arrigo and Marco Aurelio Sterpa and Alessio Sampieri and Fabio Galasso}, |
| year={2023}, |
| eprint={2301.09489}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.CV} |
| } |
| |
| @InProceedings{Acsintoae_CVPR_2022, |
| author = {Andra Acsintoae and Andrei Florescu and Mariana{-}Iuliana Georgescu and Tudor Mare and Paul Sumedrea and Radu Tudor Ionescu and Fahad Shahbaz Khan and Mubarak Shah}, |
| title = {UBnormal: New Benchmark for Supervised Open-Set Video Anomaly Detection}, |
| booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, |
| month = {June}, |
| year = {2022}, |
| } |
| ``` |
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| ## References |
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| [1] A. Acsintoae, A. Florescu, M.-I. Georgescu, T. Mare, P. Sumedrea, R. T. Ionescu, F. S. Khan, M. Shah, Ubnormal: New benchmark for supervised open-set video anomaly detection, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022, pp.20143–20153. |
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| [2] H.-S. Fang, S. Xie, Y.-W. Tai, C. Lu, Rmpe: Regional multi-person pose estimation, in: ICCV, 2017, pp. 2334–2343. |
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| [3] Y. Xiu, J. Li, H. Wang, Y. Fang, C. Lu, Pose Flow: Efficient online pose tracking, in: BMVC, 2018. |
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