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Counting Pedestrian with Mixed Features and Extreme Learning Machine

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Abstract

An approach for estimating the number of pedestrians is presented in this paper. The proposed counting framework combines two main pedestrian counting strategies—direct approach and indirect approach—by the use of mixed features and extreme learning machine (ELM). ELM is used to map mixed features to the number of pedestrians. Mixed features consist of holistic low-level features and rectangular local binary pattern (rLBP) features, and rLBP features are new features designed to describe the statistical and structural information of explicit pedestrian detection rectangles. Through mixed features, the information from both direct approach (rLBP features) and indirect approach (low-level features) is used in our algorithm, so we can take full advantage of two counting strategies. The detection rectangles are obtained by the use of the pedestrian detector described in paper “the fastest pedestrian detector in the west" (FPDW) by Dollár et al. Based on integral channel features and soft cascade classifier, FPDW is able to provide outstanding detection results at rapid speed. Experimental results on PETS 2009 datasets show that the proposed counting framework can improve counting accuracy significantly by the combination of two counting strategies. rLBP features are effective to describe the useful information of detection rectangles for regression models, and mixed features are more effective than either of both.

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Acknowledgments

This work is supported by the Opening Fund of Top key Discipline of Computer Software and Theory in Zhejiang Provincial Colleges at Zhejiang Normal University (Grant No. ZSDZZZZXK38), the National Nature Science Foundation of China (Grant Nos. 60970034, 61170287, 61232016), and the Foundation for the Author of National Excellent Doctoral Dissertation (Grant No. 2007B4).

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Authors and Affiliations

  1. College of Computer, National University of Defense Technology, Changsha, 410073, China

    Yuanwei Li & En Zhu

  2. Top Key Discipline of Computer Software and Theory, Zhejiang Provincial College, Zhejiang Normal University, Jinhua, 321004, China

    Xinzhong Zhu & Jianmin Zhao

  3. State Key Laboratory of High Performance Computing, National University of Defense Technology, Changsha, 410073, China

    Jianping Yin

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  1. Yuanwei Li
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  2. En Zhu
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  3. Xinzhong Zhu
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  4. Jianping Yin
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  5. Jianmin Zhao
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Correspondence to Jianmin Zhao.

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Li, Y., Zhu, E., Zhu, X. et al. Counting Pedestrian with Mixed Features and Extreme Learning Machine. Cogn Comput 6, 462–476 (2014). https://doi.org/10.1007/s12559-014-9248-1

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