Official pytorch implementation for the paper entitled "Video Anomaly Detection by Solving Decoupled Spatio-Temporal Jigsaw Puzzles" (ECCV 2022)
Please make sure that you have sufficient storage.
python gen_patches.py --dataset shanghaitech --phase test --filter_ratio 0.8 --sample_num 9
Dataset | # Patch (train) | # Patch (test) | filter ratio | sample num | storage |
---|---|---|---|---|---|
Ped2 | 27660 | 31925 | 0.5 | 7 | 20G |
Avenue | 96000 | 79988 | 0.8 | 7 | 58G |
Shanghaitech | 145766 | 130361 | 0.8 | 9 | 119G |
python main.py --dataset shanghaitech --val_step 100 --print_interval 20 --batch_size 192 --sample_num 9 --epochs 100 --static_threshold 0.2
python main.py --dataset shanghaitech/avenue/ped --sample_num 9/7/7 --checkpoint xxx.pth
We provide the pre-trained weights for STC, Avenue and Ped2.
@inproceedings{wang2022jigsaw-vad,
title = {Video Anomaly Detection by Solving Decoupled Spatio-Temporal Jigsaw Puzzles},
author = {Guodong Wang and Yunhong Wang and Jie Qin and Dongming Zhang and Xiuguo Bao and Di Huang},
booktitle = {European Conference on Computer Vision (ECCV)},
year = {2022}
}