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HF2-VAD

Offcial implementation of "A Hybrid Video Anomaly Detection Framework via Memory-Augmented Flow Reconstruction and Flow-Guided Frame Prediction, ICCV-2021".

[Paper] [Supp] [arXiv]

pipeline

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}
}

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