wufan-tb / oaad

Object and Action Anomaly Detection

Object and Action Anomaly Detection

Intorduction

This is a PyToch implementation of Object and Action Anomaly Detection Using Pretrain Models.

Framework:

Contributions

• we use object detection and action detection pretrain models(on coco and ava) to extrat features, which is good enough to do anomaly detection.
• in training steps, we use GMM to cluster softlabel features, in inference steps, we calculate feature's probability as it's anomaly score.
• our method achieved good balance between accuracy and speed, compared with SOTA method.

Method Type	Methods	Ped2	Avenue	SHTech	Speed
Image Reconstruction	Hyunjong et al. [1] (CVPR2020)	90.2	82.8	69.8	>67fps
	*Ours(object label only)	93.1	/	70.9	70fps
Frame Prediction	Hyunjong et al. [1] (CVPR2020)	97.0	88.5	70.5	67fps
Pretrain Model	Radu et al. [2] (CVPR 2019)	97.8	90.4	84.9	11fps
	*Ours(object and action label)				29fps

Anomaly Detection Demo(SHTech dataset)

Dataset and Model Weights Download

• USCD Ped2 [dataset]
• CUHK Avenue [dataset]
• ShanghaiTech [dataset]
• yolov5 [weights file]
• deepsort [weights file]

Installation and Usage

1. prepare environment:

   conda create -n oaad python=3.7.11
   conda activate oaad
   pip install -r requirements.txt
   git clone https://github.com/wufan-tb/oaad
   

2. evaluate our method with only object detection pretrain model:

   python yolo_AD.py --dataset {your dataset path}
   

3. evaluate our method with object and action detection pretrain models:

   python yolo_slowfast_AD.py --dataset {your dataset path}
   

References

[1] Hyunjong Park, Jongyoun Noh, Bumsub Ham. Learning Memory-guided Normality for Anomaly Detection. In Proceedings of CVPR, pages 14372–14381, 2020.

[2] Radu Tudor Ionescu, Fahad Shahbaz Khan, Mariana-Iuliana Georgescu, Ling Shao. Object-centric Auto-encoders and Dummy Anomalies for Abnormal Event Detection in Video. In Proceedings of CVPR, pages 7842–7851, 2019.

Citation

If you find our work useful, please cite as follow:

{   oaad,
    author = {Wu Fan},
    title = { Object and Action Anomaly Detection },
    year = {2021},
    url = {\url{https://github.com/wufan-tb/oaad}}
}