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Pedestrian Detection Using Deep Channel Features in Monocular Image Sequences

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Neural Information Processing (ICONIP 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9949))

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Abstract

In this paper, we propose the Deep Channel Features as an extension to Channel Features for pedestrian detection. Instead of using hand-crafted features, our method automatically learns deep channel features as a mid-level feature by using a convolutional neural network. The network is pretrained by the unsupervised sparse filtering and a group of filters is learned for each channel. Combining the learned deep channel features with other low-level channel features (i.e. LUV channels, gradient magnitude channel and histogram of gradient channels) as the final feature, a boosting classifier with depth-2 decision tree as the weak classifier is learned. Our method achieves a significant detection performance on public datasets (i.e. INRIA, ETH, TUD, and CalTech).

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Acknowledgments

This work was supported in part by the Natural Science Foundation of China (NSFC) under Grant No. 61472038 and No. 61375044.

Author information

Authors and Affiliations

  1. Beijing Lab of Intelligent Information, School of Computer Science, Beijing Institute of Technology, Beijing, 100081, People’s Republic of China

    Zhao Liu, Yang He, Yi Xie, Hongyan Gu, Chao Liu & Mingtao Pei

  2. People’s Public Security University of China, Beijing, 100038, People’s Republic of China

    Zhao Liu & Yi Xie

Authors
  1. Zhao Liu
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  2. Yang He
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  3. Yi Xie
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  4. Hongyan Gu
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  5. Chao Liu
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  6. Mingtao Pei
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Corresponding author

Correspondence to Mingtao Pei .

Editor information

Editors and Affiliations

  1. The University of Tokyo , Tokyo, Japan

    Akira Hirose

  2. Kobe University , Kobe, Japan

    Seiichi Ozawa

  3. Okinawa Institute of Science and Technology Graduate University, Onna, Japan

    Kenji Doya

  4. Nara Institute of Science and Technology , Ikoma, Japan

    Kazushi Ikeda

  5. Kyungpook National University , Daegu, Korea (Republic of)

    Minho Lee

  6. Chinese Academy of Sciences , Beijing, China

    Derong Liu

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Liu, Z., He, Y., Xie, Y., Gu, H., Liu, C., Pei, M. (2016). Pedestrian Detection Using Deep Channel Features in Monocular Image Sequences. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9949. Springer, Cham. https://doi.org/10.1007/978-3-319-46675-0_67

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  • DOI: https://doi.org/10.1007/978-3-319-46675-0_67

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46674-3

  • Online ISBN: 978-3-319-46675-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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