Adaptively adjusted gaussian mixture models for surveillance applications

Tianci Huang, Xiangzhong Fang, Jingbang Qiu, Takeshi Ikenaga

研究成果: Conference contribution

5 引用 (Scopus)

抜粋

Segmentation of moving objects is the basic step for surveillance system. The Gaussian Mixture Model is one of the best models to cope with repetitive motions in a dynamic and complex environment. In this paper, an Adaptively Adjustment Mechanism was proposed by fully utilizing Gaussian distributions with least number so as to save the amount of computation. In addition to that, by applying proposed Gaussian Mixture Model scheme to edge segmented image and combining with data fusion method, the proposed algorithm was able to resist illumination change in scene and remove shadows of motion. Experiments proved the excellent performance.

元の言語English
ホスト出版物のタイトルAdvances in Multimedia Modeling - 16th International Multimedia Modeling Conference, MMM 2010, Proceedings
ページ689-694
ページ数6
DOI
出版物ステータスPublished - 2009 12 1
イベント16th International Multimedia Modeling Conference on Advances in Multimedia Modeling, MMM 2010 - Chongqing, China
継続期間: 2010 10 62010 10 8

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
5916 LNCS
ISSN(印刷物)0302-9743
ISSN(電子版)1611-3349

Conference

Conference16th International Multimedia Modeling Conference on Advances in Multimedia Modeling, MMM 2010
China
Chongqing
期間10/10/610/10/8

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • これを引用

    Huang, T., Fang, X., Qiu, J., & Ikenaga, T. (2009). Adaptively adjusted gaussian mixture models for surveillance applications. : Advances in Multimedia Modeling - 16th International Multimedia Modeling Conference, MMM 2010, Proceedings (pp. 689-694). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); 巻数 5916 LNCS). https://doi.org/10.1007/978-3-642-11301-7_70