Dynamic Local Aggregation Network with Adaptive Clusterer for Anomaly Detection (ECCV 2022)

May 29, 2023 · View on GitHub

This repository is the official implementation of the ECCV 2022 paper "Dynamic Local Aggregation Network with Adaptive Clusterer for Anomaly Detection" with pytorch. [PDF] [Arxiv]

Requirements

Python 3.8
PyTorch 1.9.0
Numpy
Sklearn
minisom (https://github.com/JustGlowing/minisom)

Datasets

USCD Ped2 [dataset]
CUHK Avenue [dataset]
ShanghaiTech [dataset]

These datasets are from an official github of "Future Frame Prediction for Anomaly Detection - A New Baseline (CVPR 2018)".

Download the datasets into data folder, like ./data/ped2/

AC pre-clustering

git clone https://github.com/Beyond-Zw/DLAN-AC.git
cd projects/DLAN-AC
python train.py --AC_clustering True --dataset_path 'your_dataset_directory' --dataset_type ped2 --exp_dir 'your_log_directory'

Formal training

python train.py --AC_clustering False --dataset_path 'your_dataset_directory' --dataset_type ped2 --exp_dir 'your_log_directory'

Citation

If you feel this work helpful, please cite our paper:

@article{yang2023video,
  title={Video Event Restoration Based on Keyframes for Video Anomaly Detection},
  author={Yang, Zhiwei and Liu, Jing and Wu, Zhaoyang and Wu, Peng and Liu, Xiaotao},
  journal={arXiv preprint arXiv:2304.05112},
  year={2023}
}

Acknowledgement

This repository contains modified codes from:

[MNAD]
[MPN]

We sincerely thank the owners of all these great repos!