This is the official implementation of Attention-based Residual Autoencoder for Video Anomaly Detection.
- Linux or macOS
- Python 3
- PyTorch 1.7.0
The code can be run with Python 3.6 and above.
Install the required packages:
pip install -r requirements.txt
Clone this repo:
git clone https://github.com/vt-le/astnet.git
cd astnet
Please first download the pre-trained model
| Dataset | Pretrained Model |
|---|---|
| UCSD Ped2 | github / drive |
| CUHK Avenue | github / drive |
| ShanghaiTech Campus | github / drive |
After preparing a dataset, you can test the dataset by running:
python astnet.py \
--cfg /path/to/config/file \
--model-file /path/to/pre-trained/model \
GPUS [{GPU_index}]
A dataset is a directory with the following structure:
dataset
├── train
│ └── ${video_id}$
| └──${frame_id}$.jpg
├── test
│ └── ${video_id}$
| └──${frame_id}$.jpg
└── $dataset$.mat
If you find our work useful for your research, please consider citing:
@article{le2022attention,
title={Attention-based residual autoencoder for video anomaly detection},
author={Le, Viet-Tuan and Kim, Yong-Guk},
journal={Applied Intelligence},
pages={1--15},
year={2022},
publisher={Springer}
}For any question, please file an issue or contact:
Viet-Tuan Le: [email protected]