UR-DMU

This repo contains the Pytorch implementation of our paper:

Dual Memory Units with Uncertainty Regulation for Weakly Supervised Video Anomaly Detection

Hang Zhou, Junqing Yu, Wei Yang

• Accepted at AAAI 2023.
  

Training

Setup

We use the extracted I3D features for UCF-Crime and XD-Violence datasets from the following works:

UCF-Crime 10-crop I3D features

XD-Violence 5-crop I3D features

best performance ckpt for UCF

best performance ckpt for XD

You can also use the I3D model to extract features from preprocess.

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

• Change the file paths to the download datasets above in list/XD_Train.list and list/XD_Test.list.
• Feel free to change the hyperparameters in option.py

  Train and test the UR-DMU

  After the setup, simply run the following command:

start the visdom for visualizing the training phase

python -m visdom.server -p "port"(we use 2022)

Traing and infer for XD dataset

python xd_main.py
python xd_infer.py

Traing and infer for UCFC dataset

python ucf_main.py
python ucf_infer.py

References

We referenced the repos below for the code.

• RTFM
• XDVioDet

Citation

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

@article{URDMU_zh,
  title={Dual Memory Units with Uncertainty Regulation for Weakly Supervised Video Anomaly Detection},
  author={Zhou, Hang and Yu, Junqing and Yang, Wei},
  booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence (AAAI)},
  year={2023}
}