Non-liner learning for mixture of Gaussians

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

研究成果: 書貢獻/報告類型會議論文篇章同行評審

摘要

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.

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主出版物標題2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2013
DOIs
出版狀態已出版 - 2013
事件2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2013 - Kaohsiung, Taiwan
持續時間: 29 10月 20131 11月 2013

出版系列

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

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???event.eventtypes.event.conference???2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2013
國家/地區Taiwan
城市Kaohsiung
期間29/10/131/11/13

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