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http://dx.doi.org/10.7840/KICS.2012.37A.2.95

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

An, Tae-Ki (한국철도기술연구원 도시철도표준화연구단)
Ahn, Seong-Je (서울과학기술대학교 NID융합기술대학원)
Park, Kwang-Young (하이트론씨스템즈(주))
Park, Goo-Man (서울과학기술대학교 전자IT미디어공학과)
Publication Information
The Journal of Korean Institute of Communications and Information Sciences / v.37, no.2A, 2012 , pp. 95-104 More about this Journal
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.
Keywords
AdaBoost; Crowdedness; Pattern Recognition; Ensemble Classifier;
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