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Fast detection of abnormal events in videos with binary features

conference contribution
posted on 2018-09-13, 00:00 authored by R Leyva, V Sanchez, Chang-Tsun LiChang-Tsun Li
Millions of surveillance cameras are currently installed in public places around the world, making it necessary to intelligently analyse the acquired data to detect the occurrence of abnormal events. A vast number of methods to detect such events have been recently proposed; unfortunately, there is a lack of methods capable of detecting these events as frames are acquired, also known as online processing. In this paper, we present an online framework for video anomaly detection that employs binary features to encode motion information, and low-complexity probabilistic models for detection. Evaluation results on the popular UCSD dataset and on a recently introduced real-event video surveillance dataset show that our framework outperforms non-online and online methods.

History

Event

IEEE Signal Processing Society. Conference (2018 : Calgary, Alta.)

Series

IEEE Signal Processing Society Conference

Pagination

1318 - 1322

Publisher

Institute of Electrical and Electronics Engineers

Location

Calgary, Alta.

Place of publication

Piscataway, N.J.

Start date

2018-04-15

End date

2018-04-20

ISSN

1520-6149

ISBN-13

9781538646588

Language

eng

Publication classification

E Conference publication; E1.1 Full written paper - refereed

Copyright notice

2018, IEEE

Editor/Contributor(s)

[Unknown]

Title of proceedings

ICASSP 2018 : Proceedings of the 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing