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Video anomaly detection based on wake motion descriptors and perspective grids
conference contribution
posted on 2014-01-01, 00:00 authored by R Leyva, V Sanchez, Chang-Tsun LiChang-Tsun LiThis paper proposes a video anomaly detection method based on wake motion descriptors. The method analyses the motion characteristics of the video data, on a video volumeby- video volume basis, by computing the wake left behind by moving objects in the scene. It then probabilistically identifies those never previously seen motion patterns in order to detect anomalies. The method also considers the perspective of the scene to compensate for the relative change in an object's size introduced by the camera's view angle. To this end, a perspective grid is proposed to define the size of video volumes for anomaly detection. Evaluation results against several stateof- the-art methods show that the proposed method attains high detection accuracies and competitive computational time.
History
Event
IEEE Signal Processsing Society. Workshop (2014 : Atlanta, Ga.)Series
IEEE Signal Processsing Society WorkshopPagination
209 - 214Publisher
Institute of Electrical and Electronics EngineersLocation
Atlanta, Ga.Place of publication
Piscataway, N.J.Publisher DOI
Start date
2014-12-03End date
2014-12-05ISBN-13
9781479988822Language
engPublication classification
E1.1 Full written paper - refereedCopyright notice
2014, IEEEEditor/Contributor(s)
[Unknown]Title of proceedings
WIFS 2014 : Proceedings of the IEEE International Workshop on Information Forensics and Security 2014Usage metrics
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