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Integral Histogram with Random Projection for Pedestrian Detection

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  • Chang-Hua Liu
  • Jian-Kun Lin

Abstract

In this paper, we give a systematic study to report several deep insights into the HOG, one of the most widely used features in the modern computer vision and image processing applications. We first show that, its magnitudes of gradient can be randomly projected with random matrix. To handle over-fitting, an integral histogram based on the differences of randomly selected blocks is proposed. The experiments show that both the random projection and integral histogram outperform the HOG feature obviously. Finally, the two ideas are combined into a new descriptor termed IHRP, which outperforms the HOG feature with less dimensions and higher speed.

Suggested Citation

  • Chang-Hua Liu & Jian-Kun Lin, 2015. "Integral Histogram with Random Projection for Pedestrian Detection," PLOS ONE, Public Library of Science, vol. 10(11), pages 1-10, November.
    • Handle: RePEc:plo:pone00:0142820
    DOI: 10.1371/journal.pone.0142820
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    Cited by:

    1. Miaohui Zhang & Ming Xin, 2016. "Human Detection Using Random Color Similarity Feature and Random Ferns Classifier," PLOS ONE, Public Library of Science, vol. 11(9), pages 1-13, September.

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