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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

PWC

PWC

PWC

PyTorch Logging: wandb

The official PyTorch implementation of the paper Contracting Skeletal Kinematics for Human-Related Video Anomaly Detection, published in the Pattern Recognition journal.

teaser

Setup

Environment

conda env create -f environment.yaml
conda activate coskad

Datasets

Please take a look at the 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 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},
}