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The Treelike Assembly Classifier for Pedestrian Detection

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Intelligence and Security Informatics (PAISI 2007)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 4430))

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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.

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Authors

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Christopher C. Yang Daniel Zeng Michael Chau Kuiyu Chang Qing Yang Xueqi Cheng Jue Wang Fei-Yue Wang Hsinchun Chen

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© 2007 Springer Berlin Heidelberg

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Wei, C.X., Cao, X.B., Xu, Y.W., Qiao, H., Wang, FY. (2007). The Treelike Assembly Classifier for Pedestrian Detection. In: Yang, C.C., et al. Intelligence and Security Informatics. PAISI 2007. Lecture Notes in Computer Science, vol 4430. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71549-8_21

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  • DOI: https://doi.org/10.1007/978-3-540-71549-8_21

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-71548-1

  • Online ISBN: 978-3-540-71549-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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