# COSKAD

**Repository Path**: geekyzql/COSKAD

## Basic Information

- **Project Name**: COSKAD
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-02-18
- **Last Updated**: 2025-02-18

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Contracting Skeletal Kinematics for Human-Related Video Anomaly Detection
_Alessandro Flaborea*, Guido D'Amely*, Stefano D'Arrigo*, Marco Aurelio Sterpa, Alessio Sampieri, Fabio Galasso_


[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/contracting-skeletal-kinematic-embeddings-for/video-anomaly-detection-on-hr-ubnormal)](https://paperswithcode.com/sota/video-anomaly-detection-on-hr-ubnormal?p=contracting-skeletal-kinematic-embeddings-for)

[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/contracting-skeletal-kinematic-embeddings-for/anomaly-detection-on-ubnormal)](https://paperswithcode.com/sota/anomaly-detection-on-ubnormal?p=contracting-skeletal-kinematic-embeddings-for)

[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/contracting-skeletal-kinematic-embeddings-for/video-anomaly-detection-on-hr-avenue)](https://paperswithcode.com/sota/video-anomaly-detection-on-hr-avenue?p=contracting-skeletal-kinematic-embeddings-for)

[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/contracting-skeletal-kinematic-embeddings-for/video-anomaly-detection-on-hr-shanghaitech)](https://paperswithcode.com/sota/video-anomaly-detection-on-hr-shanghaitech?p=contracting-skeletal-kinematic-embeddings-for)

<p align="center">
    <a href="https://pytorch.org/get-started/locally/"><img alt="PyTorch" src="https://img.shields.io/badge/pytorch-lightning-blue.svg?logo=PyTorch%20Lightning"></a>
    <a href="https://wandb.ai/site"><img alt="Logging: wandb" src="https://img.shields.io/badge/logging-wandb-yellow"></a>

</p>


The official PyTorch implementation of the paper [**Contracting Skeletal Kinematics for Human-Related Video Anomaly Detection**](https://www.sciencedirect.com/science/article/pii/S0031320324005685), published in the Pattern Recognition journal.

<!-- Visit our [**webpage**](https://www.pinlab.org/coskad) for more details. -->

![teaser](assets/model.png)


## Setup
### Environment
```
conda env create -f environment.yaml
conda activate coskad
```

### Datasets
Please take a look at the [MoCoDAD](https://github.com/aleflabo/MoCoDAD) repo for more details.

### **Training** 

To train COSKAD, you have to select one of the different model's option presented. 
In each config file is present a section called "Model's configuration" in which the flags enable and disable different features of COSKAD:

1. use_decoder: False   if use_decoder=True, the autoencoder architecture is used (usable only in the euclidean space)
2. use_vae: False   if use_vae=True, the spherical space is used
3. hyperbolic: True     if hyperbolic=True, the hyperbolic space is used, else the euclidean space is enabled
4. static_center: False     if static_center=False, the center of the hypersphere is not updated every epoch

Update the args 'data_dir', 'test_path', 'dataset_path_to_robust' with the path where you stored the datasets.  

To train COSKAD:
```
python train_COSKAD.py --config config/[Avenue/UBnormal/STC]/{config_name}.yaml
```


### Once trained, you can run the **Evaluation**

The training config is saved the associated experiment directory (/args.exp_dir/args.dataset_choice/args.dir_name). 
In order to evaluate the model on the test set, you need to change the following parameters in the config:

- split: 'Test'
- validation: 'False'
- load_ckpt: 'path_to_the_saved_ckpt'

Test COSKAD
```
python eval_COSKAD.py --config /args.exp_dir/args.dataset_choice/args.dir_name/config.yaml
```
additional flag you can use:
- use_hr: False -> just for test. Use the entire version of the dataset or the Human-Related one.

### Visualization

We provide the code to visualize frames, poses and anomaly scores. Follow the instructions in [visualize](/visualize) for further details.


## Citation
```
@article{FLABOREA2024110817,
title = {Contracting skeletal kinematics for human-related video anomaly detection},
journal = {Pattern Recognition},
volume = {156},
pages = {110817},
year = {2024},
issn = {0031-3203},
doi = {https://doi.org/10.1016/j.patcog.2024.110817},
url = {https://www.sciencedirect.com/science/article/pii/S0031320324005685},
author = {Alessandro Flaborea and Guido Maria {D’Amely di Melendugno} and Stefano D’Arrigo and Marco Aurelio Sterpa and Alessio Sampieri and Fabio Galasso},
keywords = {Anomaly detection, Open set recognition, Hyperbolic geometry, Kinematic skeleton, Graph convolutional networks},
}
```