Awesome
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}
}