Adaptively adjusted gaussian mixture models for surveillance applications

Tianci Huang, Xiangzhong Fang, Jingbang Qiu, Takeshi Ikenaga

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationAdvances in Multimedia Modeling - 16th International Multimedia Modeling Conference, MMM 2010, Proceedings
Pages689-694
Number of pages6
DOIs
Publication statusPublished - 2009 Dec 1
Event16th International Multimedia Modeling Conference on Advances in Multimedia Modeling, MMM 2010 - Chongqing, China
Duration: 2010 Oct 62010 Oct 8

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5916 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference16th International Multimedia Modeling Conference on Advances in Multimedia Modeling, MMM 2010
CountryChina
CityChongqing
Period10/10/610/10/8

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ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Huang, T., Fang, X., Qiu, J., & Ikenaga, T. (2009). Adaptively adjusted gaussian mixture models for surveillance applications. In 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); Vol. 5916 LNCS). https://doi.org/10.1007/978-3-642-11301-7_70