Bidirectional skip-frame prediction for video anomaly detection with intra-domain disparity-driven attention (BiSP)
Published in Pattern Recognition.
python 3.8
torch 1.13
-
Data preparation
Ped1/Ped2: http://www.svcl.ucsd.edu/projects/anomaly/dataset.htm
Avenue: https://www.cse.cuhk.edu.hk/leojia/projects/detectabnormal/dataset.html
ShanghaiTech: https://svip-lab.github.io/dataset/campus_dataset.html -
Train
python Train.py # Ped2
python Train_ped1.py # Ped1
python Train_avenue.py # Avenue
python Train_shanghaitech.py # ShanghaiTech -
Evaluation
python Evaluate.py # Ped2
python Evaluate_ped1.py # Ped1
python Evaluate_avenue.py # Avenue
python Evaluate_shanghaitech.py # ShanghaiTech
You can download the pretrained weights of BiSP for the four datasets from Google.
If you use this work, please cite:
@article{lyu2025bidirectional,
title={Bidirectional skip-frame prediction for video anomaly detection with intra-domain disparity-driven attention},
author={Lyu, Jiahao and Zhao, Minghua and Hu, Jing and Xi, Runtao and Huang, Xuewen and Du, Shuangli and Shi, Cheng and Ma, Tian},
journal={Pattern Recognition},
pages={112010},
year={2025},
publisher={Elsevier}
}