This repo contains the Pytorch implementation of our paper: Weakly Supervised Video Anomaly Detection via Self-Guided Temporal Discriminative Transformer
Accepted at TCYB 2022.
Please download the extracted I3d features and checkpoint for ShanghaiTech dataset for a demo:
You should change following files:
(1) Change the file paths to the download datasets above in list/shanghai-i3d-test.list and list/shanghai-i3d-train.list.
(2) Move checkpoint file into path './ckpt_final/'.
(3) Change the hyperparameters in src/option.py if you like.
You can run 'python main.py' to train a model, or run 'python test_cur.py' to test a trained model.
If you find this repo useful for your research, please cite our paper:
@article{huang2022weakly,
title={Weakly Supervised Video Anomaly Detection via Self-Guided Temporal Discriminative Transformer},
author={Huang, Chao and Liu, Chengliang and Wen, Jie and Wu, Lian and Xu, Yong and Jiang, Qiuping and Wang, Yaowei},
journal={IEEE Transactions on Cybernetics},
year={2022},
publisher={IEEE}
}