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Video anomaly detection and localization by local motion based joint video representation and OCELM

Wang, Siqi; Zhu, En; Yin, Jianping; Porikli, Fatih


Nowadays, human-based video analysis becomes increasingly exhausting due to the ubiquitous use of surveillance cameras and explosive growth of video data. This paper proposes a novel approach to detect and localize video anomalies automatically. For video feature extraction, video volumes are jointly represented by two novel local motion based video descriptors, SL-HOF and ULGP-OF. SL-HOF descriptor captures the spatial distribution information of 3D local regions’ motion in the spatio-temporal...[Show more]

CollectionsANU Research Publications
Date published: 2018
Type: Journal article
Source: Neurocomputing
DOI: 10.1016/j.neucom.2016.08.156
Access Rights: Open Access


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