On Stable Dynamic Background Generation Technique using Gaussian Mixture Models for Robust Object Detection

Mahfuzul Haque, Manzur Murshed, Manoranjan Paul

Research output: Book chapter/Published conference paperConference paperpeer-review

29 Citations (Scopus)

Abstract

Gaussian mixture models (GMM) is used to represent the dynamic background in a surveillance video to detect the moving objects automatically. All the existing GMM based techniques inherently use the proportion by which a pixel is going to observe the background in any operating environment. In this paper we first show that such a proportion not only varies widely across different scenarios but also forbids using very fast learning rate. We then propose a dynamic background generation technique in conjunction with basic background subtraction which detected moving objects with improved stability and superior detection quality on a wide range of operating environments in two sets of benchmark surveillance sequences.
Original languageEnglish
Title of host publicationAVSS2008
Place of PublicationUSA
PublisherIEEE
Pages41-48
Number of pages8
ISBN (Electronic)9780769533414
DOIs
Publication statusPublished - 2008
EventIEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) - Santa Fe, New Mexico
Duration: 01 Sep 200803 Sep 2008

Conference

ConferenceIEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)
Period01/09/0803/09/08

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