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Pedestrian Detection with Deep Convolutional Neural Network

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Computer Vision - ACCV 2014 Workshops (ACCV 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9008))

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Abstract

The problem of pedestrian detection in image and video frames has been extensively investigated in the past decade. However, the low performance in complex scenes shows that it remains an open problem. In this paper, we propose to cascade simple Aggregated Channel Features (ACF) and rich Deep Convolutional Neural Network (DCNN) features for efficient and effective pedestrian detection in complex scenes. The ACF based detector is used to generate candidate pedestrian windows and the rich DCNN features are used for fine classification. Experiments show that the proposed approach achieved leading performance in the INRIA dataset and comparable performance to the state-of-the-art in the Caltech and ETH datasets.

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Acknowledgement

This work was supported in Part by National Basic Research Program of China (973 Program) with Nos. 2011CB706900, 2010CB731800, and National Science Foundation of China with Nos. 61039003, 61271433 and 61202323.

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Authors and Affiliations

  1. School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Science, Beijing, China

    Xiaogang Chen, Pengxu Wei, Wei Ke, Qixiang Ye & Jianbin Jiao

Authors
  1. Xiaogang Chen
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  2. Pengxu Wei
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  3. Wei Ke
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  4. Qixiang Ye
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  5. Jianbin Jiao
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Corresponding author

Correspondence to Jianbin Jiao .

Editor information

Editors and Affiliations

  1. Center for Visual Information Technology, International Institute of Information Technology, Hyderabad, India

    C.V. Jawahar

  2. Institue of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Shiguang Shan

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Chen, X., Wei, P., Ke, W., Ye, Q., Jiao, J. (2015). Pedestrian Detection with Deep Convolutional Neural Network. In: Jawahar, C., Shan, S. (eds) Computer Vision - ACCV 2014 Workshops. ACCV 2014. Lecture Notes in Computer Science(), vol 9008. Springer, Cham. https://doi.org/10.1007/978-3-319-16628-5_26

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  • DOI: https://doi.org/10.1007/978-3-319-16628-5_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16627-8

  • Online ISBN: 978-3-319-16628-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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