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 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. In experiments using the INRIA Person Dataset, SCHOG is evaluated in comparison with the conventional CoHOG.