Statistical and entropy based abnormal motion detection

C. P. Lee, K. M. Lim, W. L. Woon

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

3 Citations (Scopus)

Abstract

As visual surveillance systems are gaining wider usage in a variety of fields, they need to be embedded with the capability to interpret scenes automatically, which is known as human motion analysis (HMA). However, existing HMA methods are too domain specific and computationally expensive. This paper proposes a general purpose HMA method. It is based on the idea that human beings tend to exhibit random motion patterns during abnormal situations. Hence, angular and linear displacements of limb movements are characterized using basic statistical quantities. In addition, it is enhanced with the entropy of the Fourier spectrum to measure the randomness of the abnormal behavior. Various experiments have been conducted and prove that the proposed method has very high classification accuracy in identifying anomalous behavior.

Original languageEnglish
Title of host publicationProceeding, 2010 IEEE Student Conference on Research and Development - Engineering
Subtitle of host publicationInnovation and Beyond, SCOReD 2010
Pages192-197
Number of pages6
DOIs
Publication statusPublished - 2010
Externally publishedYes
Event2010 8th IEEE Student Conference on Research and Development - Engineering: Innovation and Beyond, SCOReD 2010 - Kuala Lumpur, Malaysia
Duration: 13 Dec 201014 Dec 2010

Publication series

NameProceeding, 2010 IEEE Student Conference on Research and Development - Engineering: Innovation and Beyond, SCOReD 2010

Conference

Conference2010 8th IEEE Student Conference on Research and Development - Engineering: Innovation and Beyond, SCOReD 2010
Country/TerritoryMalaysia
CityKuala Lumpur
Period13/12/1014/12/10

Keywords

  • Entropy
  • Image processing
  • Motion analysis
  • Neural networks

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Education

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