HF2-VAD
Offcial implementation of "A Hybrid Video Anomaly Detection Framework via Memory-Augmented Flow Reconstruction and Flow-Guided Frame Prediction, ICCV-2021".
1. Dependencies
python==3.6
pytorch==1.5.1
mmcv-full==1.3.1
mmdet==2.11.0
scikit-learn==0.23.2
edflow==0.4.0
PyYAML==5.4.1
tensorboardX==2.4
2. Usage
2.1 Data preparation
Please follow the instructions to prepare the training and testing dataset.
2.2 Train
We train the ML-MemAE-SC at first, then train CVAE model with the reconstructed flows, and finally finetune the whole framework. All the config files are located at ./cfgs.
To train the ML-MemAE-SC, run:
$ python ml_memAE_sc_train.py
To train the CVAE model with reconstructed flows, run:
$ python trian.py
And finetune the whole HF2VAD framework together as:
$ python finetune.py
For different datasets, please modify the configuration files accordingly.
2.3 Evaluation
To evaluation the anomaly detection performance of the trained model, run:
$ python eval.py [--model_save_path] [--cfg_file]
E.g., for the ped2 dataset:
$ python eval.py \
--model_save_path=./pretrained_ckpts/ped2_HF2VAD_99.31.pth \
--cfg_file=./pretrained_ckpts/ped2_HF2VAD_99.31_cfg.yaml
You can download the pretrained weights of HF2VAD for Ped2, Avenue and ShanghaiTech datasets from here.
3. Results
| Model | UCSD Ped2 | CUHK Avenue | ShanghaiTech |
|---|---|---|---|
| HF2-VAD | 99.3% | 91.1% | 76.2% |
Acknowledgment
We thank jhaux for the PyTorch implementation of the conditional VAE.
Citation
If you find this repo useful, please consider citing:
@inproceedings{liu2021hf2vad,
title = {A Hybrid Video Anomaly Detection Framework via Memory-Augmented Flow Reconstruction and Flow-Guided Frame Prediction},
author = {Liu, Zhian and Nie, Yongwei and Long, Chengjiang and Zhang, Qing and Li, Guiqing},
booktitle={Proceedings of the IEEE International Conference on Computer Vision},
year = {2021}
}
The problems are as follows
（出现的问题如下所示）
31 Dec 22, 2022
3 Oct 6, 2022
6.3k Dec 30, 2022
770 Jan 2, 2023
3.9k Jan 5, 2023
141 Dec 30, 2022
28 Nov 25, 2022
364 Dec 28, 2022
55 Dec 29, 2022
251 Dec 8, 2022
30 Jan 4, 2023
59 Nov 24, 2022
101 Nov 25, 2022
49 Nov 23, 2022
565 Jan 4, 2023
48 Sep 27, 2022
76 Nov 23, 2022
72 Nov 9, 2022