Non-liner learning for mixture of Gaussians

Chih Yang Lin, Pin Hsian Liu, Tatenda Muindisi, Chia Hung Yeh, Po Chyi Su

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

Abstract

Background modeling plays a key role of event detection in intelligent surveillance systems. Gaussian Mixture Model (GMM) is the wide-used background modeling method in latest surveillance systems. However, the model has some disadvantageous when the object moves slowly. In this paper, we propose a mechanism which takes the advantage of Gaussian error function (ERF) to adjust the growths of each Gaussian's weights and variances, to solve the problem that traditional GMM misjudged the slow moving object as background. The mechanism improves the GMM model to detect the slow moving object accurately and enhance the robustness of surveillance systems.

Original languageEnglish
Title of host publication2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2013
DOIs
StatePublished - 2013
Event2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2013 - Kaohsiung, Taiwan
Duration: 29 Oct 20131 Nov 2013

Publication series

Name2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2013

Conference

Conference2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2013
Country/TerritoryTaiwan
CityKaohsiung
Period29/10/131/11/13

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