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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 Li
This 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

Pagination

209-214

Location

Atlanta, Ga.

Start date

2014-12-03

End date

2014-12-05

ISBN-13

9781479988822

Language

eng

Publication classification

E1.1 Full written paper - refereed

Copyright notice

2014, IEEE

Editor/Contributor(s)

[Unknown]

Title of proceedings

WIFS 2014 : Proceedings of the IEEE International Workshop on Information Forensics and Security 2014

Event

IEEE Signal Processsing Society. Workshop (2014 : Atlanta, Ga.)

Publisher

Institute of Electrical and Electronics Engineers

Place of publication

Piscataway, N.J.

Series

IEEE Signal Processsing Society Workshop

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