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People Detection by Boosting Features in Nonlinear Subspace

  • Jie Yang
  • Jinqiao Wang
  • Hanqing Lu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6297)

Abstract

In this paper, we propose a novel approach to detect people by boosting features in the nonlinear subspace. Firstly, three types of the HOG (Histograms of Oriented Gradients) descriptor are extracted and grouped into one descriptor to represent the samples. Then, the nonlinear subspace with higher dimension is constructed for positive and negative samples respectively by using Kernel PCA. The final features of the samples are derived by projecting the grouped HOG descriptors onto the nonlinear subspace. Finally, AdaBoost is used to select the discriminative features in the nonlinear subspace and train the detector. Experimental results demonstrate the effectiveness of the proposed method.

Keywords

Training Sample Integral Image High Dimensional Feature Space Kernel Principal Component Analysis Oriented Gradient 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Jie Yang
    • 1
  • Jinqiao Wang
    • 1
  • Hanqing Lu
    • 1
  1. 1.National Laboratory of Pattern Recognition, Institute of AutomationChinese Academy of SciencesBeijingChina

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