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Pedestrian Detection Fusing HOG Based on LE and Haar-Like Feature

  • Jin Huang
  • Bo Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10956)

Abstract

In order to change the low detecting speed and excess redundant information of the gradient histogram (HOG), this paper proposes the pedestrian detection fusing HOG based on Laplacian Eigenmaps (LE) dimensionality reduction and Haar-Like feature. By constructing a pedestrian detection sample set mixed with HOG and Haar-Like feature set, and using LE to reduce the HOG feature, then fuse Haar-Like feature extraction with HOG-LE feature. Finally, we use Adaboost cascade strong classifier to test INRIA Person sample library, the test results are obviously better than the single feature extraction method in terms of detection rate, and the text also compares the method with some other pedestrian detection methods. The pedestrian detection fusing HOG based on LE reduced dimension and Haar-Like features proposed in this paper improves detection rate effectively in real scenarios.

Keywords

HOG Haar-Like LE Adaboost Pedestrian detection 

Notes

Acknowledgment

This work was supported by the National Natural Science Foundation of China (Grant no. 61273303, 61572381). The authors would like to thank all the editors and reviewers for their valuable comments and suggestions.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System, College of Computer Science and TechnologyWuhan University of Sciences and TechnologyWuhanChina

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