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A Two-Stage Approach for Bag Detection in Pedestrian Images

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Computer Vision -- ACCV 2014 (ACCV 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9006))

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Abstract

Bag detection in pedestrian images is a very practical visual surveillance problem. It is challenging because bag appearance may vary greatly. In this paper, we propose a novel two-stage approach for bag detection in pedestrian images. Firstly, we utilize two stripe vocabulary forests to check whether a pedestrian is with a bag. Secondly, we locate the bag location by ranking the generated bottom-up region proposals. The ranker is learned with a convolutional neural network (CNN). Experiments are performed on a subset of CUHK person re-identification dataset that show the effectiveness of our approach for bag detection in pedestrian images. Although developed for a specific problem, our approach could be applied to detect other carrying objects in pedestrian images.

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Acknowledgement

This work is supported in part by National Basic Research Program of China under Grant No.2011CB302203, and it is also supported by a grant from OMRON Corporation.

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Authors and Affiliations

  1. Computer Science and Technology Department, Tsinghua University, Beijing, China

    Yuning Du & Haizhou Ai

  2. OMRON Social Solutions Co., LTD, Tokyo, Japan

    Shihong Lao

Authors
  1. Yuning Du
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  2. Haizhou Ai
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  3. Shihong Lao
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Corresponding author

Correspondence to Yuning Du .

Editor information

Editors and Affiliations

  1. Technische Universität München, Garching, Bayern, Germany

    Daniel Cremers

  2. University of Adelaide, Adelaide, South Australia, Australia

    Ian Reid

  3. Keio University, Yokohama, Kanagawa, Japan

    Hideo Saito

  4. University of California at Merced, Merced, California, USA

    Ming-Hsuan Yang

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© 2015 Springer International Publishing Switzerland

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Du, Y., Ai, H., Lao, S. (2015). A Two-Stage Approach for Bag Detection in Pedestrian Images. In: Cremers, D., Reid, I., Saito, H., Yang, MH. (eds) Computer Vision -- ACCV 2014. ACCV 2014. Lecture Notes in Computer Science(), vol 9006. Springer, Cham. https://doi.org/10.1007/978-3-319-16817-3_33

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  • DOI: https://doi.org/10.1007/978-3-319-16817-3_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16816-6

  • Online ISBN: 978-3-319-16817-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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