Robust background segmentation using background models for surveillance application

Tianci Huang*, Jingbang Qiu, Takahiro Sakayori, Takeshi Ikenaga

*この研究の対応する著者

研究成果: Conference contribution

5 被引用数 (Scopus)

抄録

Gaussian Mixture Models (GMM) is a very typical method for background subtraction because it possesses a strong resistibility to repetitive background motion. However when it comes to complex environment, some unexpected situations occur, e.g., when illumination changes, gradually or quickly, segmentation is generated with a poor result. Moreover, this method is not capable of distinguishing shadows of moving objects. In this paper features of intensity and texture information are utilized to eliminate the shadow of moving objects. Integrated with modified Gaussian mixture models by redefining the update criterion, proposed algorithm is adapted to the flexible illumination environment. To validate that the proposed algorithm is robust to apply on surveillance system, we provide a metric with set of variables for evaluation, a comparison had been made between proposal and original GMM, results show the accuracy improvement of models using our updated algorithm. Averagely at least of 34.8% decrease of false alarm rate proves the quality of segmentation has been significantly enhanced and proposal is more competent and stable for outdoor surveillance applications.

本文言語English
ホスト出版物のタイトルProceedings of the 11th IAPR Conference on Machine Vision Applications, MVA 2009
ページ402-405
ページ数4
出版ステータスPublished - 2009 12 1
イベント11th IAPR Conference on Machine Vision Applications, MVA 2009 - Yokohama, Japan
継続期間: 2009 5 202009 5 22

出版物シリーズ

名前Proceedings of the 11th IAPR Conference on Machine Vision Applications, MVA 2009

Conference

Conference11th IAPR Conference on Machine Vision Applications, MVA 2009
国/地域Japan
CityYokohama
Period09/5/2009/5/22

ASJC Scopus subject areas

  • コンピュータ ビジョンおよびパターン認識

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