Real-time abnormal motion detection in surveillance video

Nahum Kiryati, Tammy Riklin Raviv, Yan Ivanchenko, Shay Rochel

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

40 Scopus citations

Abstract

Video surveillance systems produce huge amounts of data for storage and display. Long-term human monitoring of the acquired video is impractical and ineffective. Automatic abnormal motion detection system which can effectively attract operator attention and trigger recording is therefore the key to successful video surveillance in dynamic scenes, such as airport terminals. This paper presents a novel solution for realtime abnormal motion detection. The proposed method is well-suited for modern video-surveillance architectures, where limited computing power is available near the camera for compression and communication. The algorithm uses the macroblock motion vectors that are generated in any case as part of the video compression process. Motion features are derived from the motion vectors. The statistical distribution of these features during normal activity is estimated by training. At the operational stage, improbable-motion feature values indicate abnormal motion. Experimental results demonstrate reliable real-time operation.

Original languageEnglish
Title of host publication2008 19th International Conference on Pattern Recognition, ICPR 2008
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)9781424421756
DOIs
StatePublished - 1 Jan 2008
Externally publishedYes

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651

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