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 publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages689-694
Number of pages6
Volume5916 LNCS
DOIs
Publication statusPublished - 2009
Event16th International Multimedia Modeling Conference on Advances in Multimedia Modeling, MMM 2010 - Chongqing
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)03029743
ISSN (Electronic)16113349

Other

Other16th International Multimedia Modeling Conference on Advances in Multimedia Modeling, MMM 2010
CityChongqing
Period10/10/610/10/8

Fingerprint

Gaussian Mixture Model
Surveillance
Motion
Data Fusion
Moving Objects
Resist
Gaussian distribution
Illumination
Adjustment
Segmentation
Data fusion
Lighting
Experiment
Experiments
Model

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Huang, T., Fang, X., Qiu, J., & Ikenaga, T. (2009). Adaptively adjusted gaussian mixture models for surveillance applications. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5916 LNCS, 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

Adaptively adjusted gaussian mixture models for surveillance applications. / Huang, Tianci; Fang, Xiangzhong; Qiu, Jingbang; Ikenaga, Takeshi.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 5916 LNCS 2009. p. 689-694 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5916 LNCS).

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

Huang, T, Fang, X, Qiu, J & Ikenaga, T 2009, Adaptively adjusted gaussian mixture models for surveillance applications. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 5916 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5916 LNCS, pp. 689-694, 16th International Multimedia Modeling Conference on Advances in Multimedia Modeling, MMM 2010, Chongqing, 10/10/6. https://doi.org/10.1007/978-3-642-11301-7_70
Huang T, Fang X, Qiu J, Ikenaga T. Adaptively adjusted gaussian mixture models for surveillance applications. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 5916 LNCS. 2009. p. 689-694. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-642-11301-7_70
Huang, Tianci ; Fang, Xiangzhong ; Qiu, Jingbang ; Ikenaga, Takeshi. / Adaptively adjusted gaussian mixture models for surveillance applications. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 5916 LNCS 2009. pp. 689-694 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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