A Study on Recognition of Moving Object Crowdedness Based on Ensemble Classifiers in a Sequence 


Vol. 37,  No. 2, pp. 95-104, Feb.  2012


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  Abstract

Pattern recognition using ensemble classifiers is composed of strong classifier which consists of many weak classifiers. In this paper, we used feature extraction to organize strong classifier using static camera sequence. The strong classifier is made of weak classifiers which considers environmental factors. So the strong classifier overcomes environmental effect. Proposed method uses binary foreground image by frame difference method and the boosting is used to train crowdedness model and recognize crowdedness using features. Combination of weak classifiers makes strong ensemble classifier. The classifier could make use of potential features from the environment such as shadow and reflection. We tested the proposed system with road sequence and subway platform sequence which are included in “AVSS 2007” sequence. The result shows good accuracy and efficiency on complex environment.

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  Cite this article

[IEEE Style]

T. An, S. Ahn, K. Park, G. Park, "A Study on Recognition of Moving Object Crowdedness Based on Ensemble Classifiers in a Sequence," The Journal of Korean Institute of Communications and Information Sciences, vol. 37, no. 2, pp. 95-104, 2012. DOI: .

[ACM Style]

Tae-ki An, Seong-je Ahn, Kwang-young Park, and Goo-man Park. 2012. A Study on Recognition of Moving Object Crowdedness Based on Ensemble Classifiers in a Sequence. The Journal of Korean Institute of Communications and Information Sciences, 37, 2, (2012), 95-104. DOI: .

[KICS Style]

Tae-ki An, Seong-je Ahn, Kwang-young Park, Goo-man Park, "A Study on Recognition of Moving Object Crowdedness Based on Ensemble Classifiers in a Sequence," The Journal of Korean Institute of Communications and Information Sciences, vol. 37, no. 2, pp. 95-104, 2. 2012.

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pISSN : 1226 - 4717
eISSN : 2287 - 3880