Pedestrian Detection with Sparse Depth Estimation

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著者

    • WANG Yu
    • the Graduate School of Information Science, Nagoya University
    • KATO Jien
    • the Graduate School of Information Science, Nagoya University

抄録

In this paper, we deal with the pedestrian detection task in outdoor scenes. Because of the complexity of such scenes, generally used gradient-feature-based detectors do not work well on them. We propose to use sparse 3D depth information as an additional cue to do the detection task, in order to achieve a fast improvement in performance. Our proposed method uses a probabilistic model to integrate image-feature-based classification with sparse depth estimation. Benefiting from the depth estimates, we map the prior distribution of human's actual height onto the image, and update the image-feature-based classification result probabilistically. We have two contributions in this paper: 1) a simplified graphical model which can efficiently integrate depth cue in detection; and 2) a sparse depth estimation method which could provide fast and reliable estimation of depth information. An experiment shows that our method provides a promising enhancement over baseline detector within minimal additional time.

収録刊行物

  • IEICE transactions on information and systems

    IEICE transactions on information and systems 94(8), 1690-1699, 2011-08-01

    一般社団法人 電子情報通信学会

参考文献:  17件中 1-17件 を表示

各種コード

  • NII論文ID(NAID)
    10030192716
  • NII書誌ID(NCID)
    AA10826272
  • 本文言語コード
    ENG
  • 資料種別
    ART
  • ISSN
    09168532
  • データ提供元
    CJP書誌  J-STAGE 
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