# UBnormal dataset | [COSKAD](https://github.com/aleflabo/COSKAD) | [Original Repository](https://github.com/lilygeorgescu/UBnormal)
---

Skeletal version proposed in 

**[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*
---

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.


## Notes regarding the file names' format

The UBnormal dataset annotated with the skeletal representation and its Human-Related version (HR) are released with the following directory structure:

```
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
```

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.

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}
```

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:

- `normal_scene_1_scenario1_1`
- `normal_scene_1_scenario1_10`
- `abnormal_scene_9_scenario_1_fog`
- `abnormal_scene_12_scenario_1_smoke`

In order to keep the information regarding the environment in the clip (e.g., fog, smoke, ...), this mapping has been adopted:

```
{'fog': 51, 'fire': 52, 'smoke': 53}
```


## Citation

If you find this dataset useful in your research, please consider cite:

```
@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},
  }
```


## References

[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.

[2] H.-S. Fang, S. Xie, Y.-W. Tai, C. Lu, Rmpe: Regional multi-person pose estimation, in: ICCV, 2017, pp. 2334–2343.

[3] Y. Xiu, J. Li, H. Wang, Y. Fang, C. Lu, Pose Flow: Efficient online pose tracking, in: BMVC, 2018.