Partial Derivative Guidance for Weak Classifier Mining in Pedestrian Detection

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著者

    • LIU Chang
    • the Department of Electronic Engineering, Tsinghua University
    • WANG Guijin
    • the Department of Electronic Engineering, Tsinghua University
    • LIU Chunxiao
    • the Department of Electronic Engineering, Tsinghua University
    • LIN Xinggang
    • the Department of Electronic Engineering, Tsinghua University

抄録

Boosting over weak classifiers is widely used in pedestrian detection. As the number of weak classifiers is large, researchers always use a sampling method over weak classifiers before training. The sampling makes the boosting process harder to reach the fixed target. In this paper, we propose a partial derivative guidance for weak classifier mining method which can be used in conjunction with a boosting algorithm. Using weak classifier mining method makes the sampling less degraded in the performance. It has the same effect as testing more weak classifiers while using acceptable time. Experiments demonstrate that our algorithm can process quicker than [1] algorithm in both training and testing, without any performance decrease. The proposed algorithms is easily extending to any other boosting algorithms using a window-scanning style and HOG-like features.

収録刊行物

  • IEICE transactions on information and systems

    IEICE transactions on information and systems 94(8), 1721-1724, 2011-08-01

    一般社団法人 電子情報通信学会

参考文献:  9件中 1-9件 を表示

各種コード

  • NII論文ID(NAID)
    10030192779
  • NII書誌ID(NCID)
    AA10826272
  • 本文言語コード
    ENG
  • 資料種別
    SHO
  • ISSN
    09168532
  • データ提供元
    CJP書誌  J-STAGE 
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