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Signal, Image and Video Processing

, Volume 8, Supplement 1, pp 125–134 | Cite as

An efficient HOG–ALBP feature for pedestrian detection

  • Yifeng Liu
  • Lin Zeng
  • Yan Huang
Original Paper

Abstract

Histograms of oriented gradients (HOG) is the most successful feature descriptor in pedestrian detection; however, it is limited because of only considering the gradient. It has a certain false-positive rate on some examples, which have a lot of parallel vertical components (looks like a leg or a body) due to lacking of texture feature. This paper proposes a method to combine a cell-structured HOG feature and adaptive local binary pattern feature to solve the problem that HOG is vulnerable to the interference of vertical background gradient information in pedestrian detection. In addition, we use a fast method to utilize sub-cell-based interpolation to efficiently compute HOG feature for each block. Training the combination feature to get a discriminative model by bootstrapped linear support vector machine. Experimental results on the INRIA dataset have demonstrated the effectiveness and efficiency of the proposed method.

Keywords

Pedestrian detection HOG ALBP  Vertical background gradient Bootstrapped SVM 

Notes

Acknowledgments

This work is supported by “the Fundamental Research Funds for the Central Universities” (No. 2014212020202).

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Copyright information

© Springer-Verlag London 2014

Authors and Affiliations

  1. 1.School of Electronic InformationWuhan UniversityWuhan China

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