Learning Regularity in Skeleton Trajectories for Anomaly Detection in Videos

Romero Morais, Vuong Le, Truyen Tran, Budhaditya Saha, Moussa Mansour, Svetha Venkatesh; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 11996-12004

Abstract


Appearance features have been widely used in video anomaly detection even though they contain complex entangled factors. We propose a new method to model the normal patterns of human movements in surveillance video for anomaly detection using dynamic skeleton features. We decompose the skeletal movements into two sub-components: global body movement and local body posture. We model the dynamics and interaction of the coupled features in our novel Message-Passing Encoder-Decoder Recurrent Network. We observed that the decoupled features collaboratively interact in our spatio-temporal model to accurately identify human-related irregular events from surveillance video sequences. Compared to traditional appearance-based models, our method achieves superior outlier detection performance. Our model also offers "open-box" examination and decision explanation made possible by the semantically understandable features and a network architecture supporting interpretability.

Related Material


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[bibtex]
@InProceedings{Morais_2019_CVPR,
author = {Morais, Romero and Le, Vuong and Tran, Truyen and Saha, Budhaditya and Mansour, Moussa and Venkatesh, Svetha},
title = {Learning Regularity in Skeleton Trajectories for Anomaly Detection in Videos},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}