SCHOG Feature for Pedestrian Detection

Ryuichi Ozaki, Kazunori Onoguchi


Co-occurrence Histograms of Oriented Gradients(CoHOG) has succeeded in describing the detailed shape of the object by using a co-occurrence of features. However, unlike HOG, it does not consider the difference of gradient magnitude between the foreground and the background. In addition, the dimension of the CoHOG feature is also very large. In this paper, we propose Similarity Co-occurrence Histogram of Oriented Gradients( SCHOG) considering the similarity and co-occurrence of features. Unlike CoHOG which quantize edge gradient direction to eight directions, SCHOG quantize it to four directions. Therefore, the feature dimension for the co-occurrence between edge gradient direction decreases greatly. In addition to the co-occurrence between edge gradient directions the binary code representing the similarity between features is introduced. In this paper, we use the pixel intensity, the edge gradient magnitude and the edge gradient direction as the similarity. In spite of reducing the resolution of the edge gradient direction, SCHOG realizes higher performance and lower dimension than CoHOG by adding this similarity. We have focused on pedestrian detection in this paper. However, this method is also applicable to various object recognition by introducing various kind of similarity. In experiments using the INRIA Person Dataset, SCHOG is evaluated in comparison with the conventional CoHOG.


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Paper Citation

in Harvard Style

Ozaki R. and Onoguchi K. (2014). SCHOG Feature for Pedestrian Detection . In Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-018-5, pages 60-66. DOI: 10.5220/0004813000600066

in Bibtex Style

author={Ryuichi Ozaki and Kazunori Onoguchi},
title={SCHOG Feature for Pedestrian Detection},
booktitle={Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},

in EndNote Style

JO - Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - SCHOG Feature for Pedestrian Detection
SN - 978-989-758-018-5
AU - Ozaki R.
AU - Onoguchi K.
PY - 2014
SP - 60
EP - 66
DO - 10.5220/0004813000600066