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Scale-Adaptive Deconvolutional Regression Network for Pedestrian Detection

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Computer Vision – ACCV 2016 (ACCV 2016)

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

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Abstract

Although the Region-based Convolutional Neural Network (R-CNN) families have shown promising results for object detection, they still face great challenges for task-specific detection, e.g., pedestrian detection, the current difficulties of which mainly lie in the large scale variations of pedestrians and insufficient discriminative power of pedestrian features. To overcome these difficulties, we propose a novel Scale-Adaptive Deconvolutional Regression (SADR) network in this paper. Specifically, the proposed network can effectively detect pedestrians of various scales by flexibly choosing which feature layer to regress object locations according to the height of pedestrians, thus improving the detection accuracy significantly. Furthermore, considering CNN can abstract different semantic-level features from different layers, we fuse features from multiple layers to provide both local characteristics and global semantic information of the object for final pedestrian classification, which improves the discriminative power of pedestrian features and boosts the detection performance further. Extensive experiments have verified the effectiveness of our proposed approach, which achieves the state-of-the-art log-average miss rate (MR) of 6.94% on the revised Caltech [1] and a competitive result on KITTI.

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Acknowledgment

This work was supported by 863 Program 2014AA015104, and National Science Foundation of China 61273034, and 61332016.

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Authors and Affiliations

  1. National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Yousong Zhu, Jinqiao Wang, Chaoyang Zhao, Haiyun Guo & Hanqing Lu

  2. University of Chinese Academy of Sciences, Beijing, China

    Yousong Zhu, Jinqiao Wang, Chaoyang Zhao, Haiyun Guo & Hanqing Lu

Authors
  1. Yousong Zhu
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  2. Jinqiao Wang
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  3. Chaoyang Zhao
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  4. Haiyun Guo
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  5. Hanqing Lu
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Corresponding author

Correspondence to Yousong Zhu .

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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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Zhu, Y., Wang, J., Zhao, C., Guo, H., Lu, H. (2017). Scale-Adaptive Deconvolutional Regression Network for 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_26

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  • DOI: https://doi.org/10.1007/978-3-319-54184-6_26

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  • Online ISBN: 978-3-319-54184-6

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