Paper Info
Title
Variational Abnormal Behavior Detection With Motion Consistency
Abstract
Abnormal crowd behavior detection has recently attracted increasing attention due to its wide applications in computer vision research areas. However, it is still an extremely challenging task due to the great variability of abnormal behavior coupled with huge ambiguity and uncertainty of video contents. To tackle these challenges, we propose a new probabilistic framework named variational abnormal behavior detection (VABD), which can detect abnormal crowd behavior in video sequences. We make three major contributions: (1) We develop a new probabilistic latent variable model that combines the strengths of the U-Net and conditional variational auto-encoder, which also are the backbone of our model; (2) We propose a motion loss based on an optical flow network to impose the motion consistency of generated video frames and input video frames; (3) We embed a Wasserstein generative adversarial network at the end of the backbone network to enhance the framework performance. VABD can accurately discriminate abnormal video frames from video sequences. Experimental results on UCSD, CUHK Avenue, IITB-Corridor, and ShanghaiTech datasets show that VABD outperforms the state-of-the-art algorithms on abnormal crowd behavior detection. Without data augmentation, our VABD achieves 72.24% in terms of AUC on IITB-Corridor, which surpasses the state-of-the-art methods by nearly 5%.
Year
DOI
Venue
2022
10.1109/TIP.2021.3130545
IEEE Transactions on Image Processing
Keywords
DocType
Volume
Abnormal crowd behavior detection,conditional variational auto-encoder,optical flow network,motion loss,Wasserstein generative adversarial network
Journal
31
ISSN
Citations 
PageRank 
1057-7149
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Jing Li132.44
Qingwang Huang200.34
Yingjun Du300.34
Xiantong Zhen400.34
Shengyong Chen500.68
Ling Shao600.34