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Learning regularity in skeleton trajectories for anomaly detection in videos
conference contributionposted on 2019-01-01, 00:00 authored by R Morais, Vuong Le, Truyen TranTruyen Tran, Budhaditya Saha, M Mansour, Svetha VenkateshSvetha Venkatesh
© 2019 IEEE. 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.