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Asian Conference on Computer Vision

ACCV 2014: Computer Vision - ACCV 2014 Workshops pp 513–526Cite as

Local Associated Features for Pedestrian Detection

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

Abstract

Local features are usually used to describe pedestrian appearance. While most of existing pedestrian detection methods don’t make full use of context cues, such as associated relationships between local different locations. This paper proposes two novel kinds of local associated features, gradient orientation associated feature (GOAF) and local difference of ACF (ACF-LD), to exploit context information. In our work, pedestrian samples are enlarged to contain some background regions besides human body, and GOAF, ACF and ACF-LD are combined together to describe pedestrian sample. GOAF are constructed by encoding gradient orientation features from two different positions into a single value. These two positions are come from different distance and different direction. For ACF-LD, the sample is divided into several sub regions and the ACF difference matrixes between these areas are computed to exploit the associated information between pedestrian and surrounding background. The proposed local associated features can provide complementary information for detection tasks. Finally, these features are fused with ACF to form candidate feature pool, and AdaBoost is used to select features and train a cascaded classifier of depth-two decision trees. Experimental results on two public datasets show that the proposed framework can achieve promising results compared with the state of the arts.

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Acknowledgement

This work is supported in part by National Nature Science Foundation of China: 61202209 and in part by Beijing Natural Science Foundation: 4142051.

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Authors and Affiliations

  1. Research Center for Pervasive Computing, Institute of Computing Technology Chinese Academy of Sciences, Beijing, 100190, China

    Song Shao, Hong Liu, Xiangdong Wang & Yueliang Qian

  2. Beijing Key Laboratory of Mobile Computing and Pervasive Device, Institute of Computing Technology Chinese Academy of Sciences, Beijing, 100190, China

    Song Shao, Hong Liu, Xiangdong Wang & Yueliang Qian

Authors
  1. Song Shao
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  2. Hong Liu
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  3. Xiangdong Wang
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  4. Yueliang Qian
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Corresponding author

Correspondence to Hong Liu .

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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Shao, S., Liu, H., Wang, X., Qian, Y. (2015). Local Associated Features for Pedestrian Detection. 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_37

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  • DOI: https://doi.org/10.1007/978-3-319-16628-5_37

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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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