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RTFM

This repo contains the Pytorch implementation of our paper:

Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learning

Yu Tian, Guansong Pang, Yuanhong Chen, Rajvinder Singh, Johan W. Verjans, Gustavo Carneiro.

Training

Setup

Please download the extracted I3d features for ShanghaiTech and UCF-Crime dataset from links below:

ShanghaiTech train i3d onedirve

ShanghaiTech test i3d onedrive

ShanghaiTech features on Google dirve

checkpoint for ShanghaiTech

Extracted I3d features for UCF-Crime dataset

UCF-Crime train i3d onedirve

UCF-Crime test i3d onedrive

UCF-Crime train I3d features on Google drive

UCF-Crime test I3d features on Google drive

checkpoint for Ucf-crime

The above features use the resnet50 I3D to extract from this repo.

Follow previous works, we also apply 10-crop augmentations.

The following files need to be adapted in order to run the code on your own machine:

Train and test the RTFM

After the setup, simply run the following commands:

python -m visdom.server
python main.py

Citation

If you find this repo useful for your research, please consider citing our paper:

@article{tian2021weakly,
  title={Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learning},
  author={Tian, Yu and Pang, Guansong and Chen, Yuanhong and Singh, Rajvinder and Verjans, Johan W and Carneiro, Gustavo},
  journal={arXiv preprint arXiv:2101.10030},
  year={2021}
}