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Fast Human Detection Using Deformable Part Model at the Selected Candidate Detection Positions

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Rough Sets and Knowledge Technology (RSKT 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9436))

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Abstract

We integrate the classic deformable part models (DPM) with the object proposal approaches to achieve a fast and accurate human detection system. The proposed method avoids exhaustive sliding window search, which accelerating the detection speed and reducing the incorrect false positives. In this paper, EdgeBoxes and BING are selected as the candidate object proposal methods to generate the candidate detection positions for the DPM, because their good performance and fast speed. The DPM is only carried on the candidate locations selected by EdgeBoxes and BING for fast human detection. Experiments on PASCAL 2007 dataset for human detection show that the proposed method accelerates the detection speed and reduces the incorrect detections effectively, and EdgeBoxes is better than BING.

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Authors and Affiliations

  1. Key Laboratory of Chongqing Computation and Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, 400065, People’s Republic of China

    Xiaotian Wu & Guoyin Wang

  2. Department of Information and Communication Engineering, Inha University, Incheon, 402-751, Korea

    KyoungYeon Kim & Yoo-Sung Kim

Authors
  1. Xiaotian Wu
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  2. KyoungYeon Kim
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  3. Guoyin Wang
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  4. Yoo-Sung Kim
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Corresponding author

Correspondence to Xiaotian Wu .

Editor information

Editors and Affiliations

  1. University of Milano-Bicocca, Milano, Italy

    Davide Ciucci

  2. Chongqing University of Posts and Telecommunications, Chongqing, China

    Guoyin Wang

  3. Indian Statistical Institute, Kolkata, India

    Sushmita Mitra

  4. Zhejiang Ocean University, Zhejiang, China

    Wei-Zhi Wu

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Wu, X., Kim, K., Wang, G., Kim, YS. (2015). Fast Human Detection Using Deformable Part Model at the Selected Candidate Detection Positions. In: Ciucci, D., Wang, G., Mitra, S., Wu, WZ. (eds) Rough Sets and Knowledge Technology. RSKT 2015. Lecture Notes in Computer Science(), vol 9436. Springer, Cham. https://doi.org/10.1007/978-3-319-25754-9_44

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  • DOI: https://doi.org/10.1007/978-3-319-25754-9_44

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25753-2

  • Online ISBN: 978-3-319-25754-9

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