/Anomaly_AR_Net_ICME_2020

This repository is for Weakly Supervised Video Anomaly Detection via Center-Guided Discriminative Learning(ICME 2020). The original paper can be found (https://ieeexplore.ieee.org/document/9102722) or (https://arxiv.org/abs/2104.07268)

Primary LanguagePythonMIT LicenseMIT

Introduction

This repository is for Weakly Supervised Video Anomaly Detection via Center-Guided Discriminative Learning (ICME 2020). The original paper can be found here or https://arxiv.org/abs/2104.07268. The oral video can be viewed here.

Please cite with the following BibTeX:

@inproceedings{anomaly_wan2020icme,
  title={Weakly Supervised Video Anomaly Detection via Center-Guided Discriminative Learning},
  author={Wan, Boyang and Fang, Yuming and Xia, Xue and Mei, Jiajie},
  booktitle={Proceedings of the IEEE International Conference on Multimedia and Expo},
  year={2020}
}

Requirements

  • Python 3
  • CUDA
  • numpy
  • tqdm
  • PyTorch (1.2)
  • torchvision
    Recommend: the environment can be established by running
conda env create -f environment.yaml

Data preparation

  1. Download the [i3d features]([link: https://pan.baidu.com/s/1Cn1BDw6EnjlMbBINkbxHSQ password: u4k6])(https://drive.google.com/file/d/193jToyF8F5rv1SCgRiy_zbW230OrVkuT/view?usp=sharing) and change the "dataset_path" to you/path/data

the dataset.tar file can be unzip by using tar -xvf dataset.tar

Visual Feature Extraction

if you want to extract Visual Feature like this project, you can clone this project([https://github.com/wanboyang/anomaly_feature])

Training

python main.py

The models and testing results will be created on ./ckpt and ./results respectively

Acknowledgements

Thanks the contribution of W-TALC and awesome PyTorch team.

Contact

Please contact the first author of the associated paper - Boyang Wan （wanboyangjerry@163.com) for any further queries.