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Asian Conference on Computer Vision

ACCV 2016: Computer Vision – ACCV 2016 pp 305–320Cite as

Learning to Integrate Occlusion-Specific Detectors for Heavily Occluded Pedestrian Detection

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10112))

Abstract

It is a challenging problem to detect partially occluded pedestrians due to the diversity of occlusion patterns. Although training occlusion-specific detectors can help handle various partial occlusions, it is a non-trivial problem to integrate these detectors properly. A direct combination of all occlusion-specific detectors can be affected by unreliable detectors and usually does not favor heavily occluded pedestrian examples, which can only be recognized by few detectors. Instead of combining all occlusion-specific detectors into a generic detector for all occlusions, we categorize occlusions based on how pedestrian examples are occluded into K groups. Each occlusion group selects its own occlusion-specific detectors and fuses them linearly to obtain a classifer. An L1-norm linear support vector machine (SVM) is adopted to select and fuse occlusion-specific detectors for the K classifiers simultaneously. Thanks to the L1-norm linear SVM, unreliable and irrelevant detectors are removed for each group. Experiments on the Caltech dataset show promising performance of our approach for detecting heavily occluded pedestrians.

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Acknowledgement

This work is supported in part by Singapore Ministry of Education Academic Research Fund Tier 2 MOE2015-T2-2-114 and Tier 1 RG27/14.

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Authors and Affiliations

  1. School of EEE, Nanyang Technological University, Singapore, Singapore

    Chunluan Zhou & Junsong Yuan

Authors
  1. Chunluan Zhou
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  2. Junsong Yuan
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Corresponding author

Correspondence to Chunluan Zhou .

Editor information

Editors and Affiliations

  1. National Tsing Hua University, Hsinchu, Taiwan

    Shang-Hong Lai

  2. Graz University of Technology, Graz, Austria

    Vincent Lepetit

  3. Drexel University, Philadelphia, Pennsylvania, USA

    Ko Nishino

  4. The University of Tokyo, Tokyo, Japan

    Yoichi Sato

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Zhou, C., Yuan, J. (2017). Learning to Integrate Occlusion-Specific Detectors for Heavily Occluded Pedestrian Detection. In: Lai, SH., Lepetit, V., Nishino, K., Sato, Y. (eds) Computer Vision – ACCV 2016. ACCV 2016. Lecture Notes in Computer Science(), vol 10112. Springer, Cham. https://doi.org/10.1007/978-3-319-54184-6_19

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  • DOI: https://doi.org/10.1007/978-3-319-54184-6_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-54183-9

  • Online ISBN: 978-3-319-54184-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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