ADA-VAD

Offcial implementation of "Ada-VAD: Domain Adaptable Video Anomaly Detection, SDM-2024".

[Paper]

1. Dependencies

conda env create -f VAD.yaml
conda activate VAD

2. Usage

2.1 Data preparation

Please follow the instructions to prepare the training and testing dataset.

2.2 Train

We train the Pretraining Stage mode at first, then train Adaptaion Stage model. All the config files are located at ./cfgs.

To train the Pretraining Stage model, run:

$ python unetonlyInfomaxAnypredictStage1.py -f .cfgs/unetonlyAnypredictStage1/***

To train the Adaptaion Stage model with 1-shot, run:

$ python unetonlyInfomaxAnypredictStage2.py -f .cfgs/unetonlyAnypredictStage2/***

And to train the Adaptaion Stage model with 5-shot, run:

$ python unetonlyInfomaxAnypredictStage2.py -f .cfgs/unetonlyAnypredictStage2_5shot/***

For different datasets, please modify the configuration files accordingly.

2.3 Evaluation

Each training process will be stored by tensorboard. You can still evaluation the anomaly detection performance of the trained model, run:

$ python evaluation_Anypredictstage1.py [--model_save_path] [--cfg_file] 

$ python evaluation_Anypredictstage2.py [--model_save_path] [--cfg_file] 

Acknowledgment

We thank LiUzHiAn for the PyTorch implementation of the hf2vad.

Citation

If you find this repo useful, please consider citing:

@inbook{doi:10.1137/1.9781611978032.73,
author = {Dongliang Guo and Yun Fu and Sheng Li},
title = {Ada-VAD: Domain Adaptable Video Anomaly Detection},
booktitle = {Proceedings of the 2024 SIAM International Conference on Data Mining (SDM)},
chapter = {},
pages = {634-642},
doi = {10.1137/1.9781611978032.73},
URL = {https://epubs.siam.org/doi/abs/10.1137/1.9781611978032.73},
eprint = {https://epubs.siam.org/doi/pdf/10.1137/1.9781611978032.73}
}