IIUM Repository

Human path classifier architecture

Khan, Imran Moez and Htike@Muhammad Yusof, Zaw Zaw and Khalifa, Othman Omran and Lai, Weng Kin (2011) Human path classifier architecture. In: Human Behaviour Recognition, Identification and Ccomputer Interaction. IIUM Press, Kuala Lumpur, pp. 145-153. ISBN 978-967-418-156-7

[img]
Preview
PDF (Human path classifier architecture) - Published Version
Download (458kB) | Preview

Abstract

Many surveillance systems today provide only a passive form of site monitoring. Extensive video records may be kept to help find the instigator of criminal activities after the crime has been committed but preventive measures require human involvement. In addition, there is a need for large amounts of data storage to keep up to several large volumes of video streams that may be needed for later analysis. However, monitoring and storage space are not the only concerns. Behavioral analysis itself can be applied to numerous features extracted from video sequences including path detection and other aspects of human behaviour. Up till now, path classification has been carried out mainly using Boolean logic and allows only the identification of unusual paths, and not the extent to which they are deviant from usual paths. This chapter reports on the results to solve this problem with a fuzzy inference approach to classify paths into different categories

Item Type: Book Chapter
Additional Information: 4119/21651
Uncontrolled Keywords: Human path classifier architecture
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button): Kulliyyah of Engineering > Department of Electrical and Computer Engineering
Depositing User: Prof. Dr Othman O. Khalifa
Date Deposited: 04 Sep 2012 16:37
Last Modified: 01 Jun 2015 09:20
URI: http://irep.iium.edu.my/id/eprint/21651

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year