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ROI-HOG and LBP Based Human Detection via Shape Part-Templates Matching

  • Shenghui Zhou
  • Qing Liu
  • Jianming Guo
  • Yuanyuan Jiang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7667)

Abstract

Currently, Histogram of Oriented Gradient (HOG) descriptor serves as the predominant method when it comes to human detection. To further improving its detection accuracy and decrease its large dimensions of feature vectors, we introduce an improved method in which HOG is extracted in the Region of Interest (ROI) of human body with a combined Local Binary Pattern (LBP) feature. Via establishing human shape part-templates tree, a template matching approach is employed to improve detection results and segment human edges. The experimental results on INRIA database and images from practical campus video surveillance demonstrate the effectiveness of our method.

Keywords

Pedestrian detection ROI-HOG descriptor LBP feature Shape part-template matching 

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References

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Shenghui Zhou
    • 1
  • Qing Liu
    • 1
  • Jianming Guo
    • 1
  • Yuanyuan Jiang
    • 1
  1. 1.School of AutomationWuhan University of TechnologyWuhanChina

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