The treelike assembly classifier for pedestrian detection

Authors:
C. X. Wei;X. B. Cao;Y. W. Xu;Hong Qiao;Fei-Yue Wang
Affiliations:
Department of Computer Science and Technology, University of Science and Technology of China, Hefei, P.R. China and Anhui Province Key Laboratory of Software in Computing and Communication, Hefei, ...;Department of Computer Science and Technology, University of Science and Technology of China, Hefei, P.R. China and Anhui Province Key Laboratory of Software in Computing and Communication, Hefei, ...;Department of Computer Science and Technology, University of Science and Technology of China, Hefei, P.R. China and Anhui Province Key Laboratory of Software in Computing and Communication, Hefei, ...;Institute of Automation, Chinese Academy of Sciences, Beijing, P.R. China;Institute of Automation, Chinese Academy of Sciences, Beijing, P.R. China
Venue:
PAISI'07 Proceedings of the 2007 Pacific Asia conference on Intelligence and security informatics
Year:
2007

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Abstract

Until now, classification is a primary technology in Pedestrian Detection. However, most existing single-classifiers and cascaded classifiers can hardly satisfy practical needs (e.g. false negative rate, false positive rate and detection speed). In this paper, we proposed an assembly classifier which was specifically designed for pedestrian detection in order to get higher detection rate and lower false positive rate at high speed. The assembly classifier is trained to select out the best single-classifiers, all of which will be arranged in a proper structure; finally, a treelike classifier is obtained. The experimental results have validated that the proposed assembly classifier generates better results than most of the existing single-classifiers and cascaded classifiers.