SCHOG Feature for Pedestrian Detection

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. 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, SCHOG quantize edge gradient direction to four directions. Therefore, the feature dimension for the co-occurrence between edge gradient direction decreases greatly. In addition, the binary code representing the similarity between features is introduced. In spite of reducing the resolution of the edge gradient direction, SCHOG realizes higher performance and lower dimension than CoHOG by adding this similarity. In experiments using the INRIA Person Dataset, SCHOG is evaluated in comparison with the conventional CoHOG.