Object detection based on multiresolution CoHOG

Abstract

Recently, co-occurrence histograms of oriented gradients (CoHOG), a method for describing image features in order to calculate the co-occurrence of pixels allocated at the local level, has attracted attention as an effective method for object detection. However, there are some problems. For feature descriptions that focus on individual pixels, the calculation cost and the number of dimensions tends to increase exponentially with the number of pixels. This paper proposes the multiresolution co-occurrence histograms of oriented gradients (MRCoHOG) as a feature descriptionmethod that is able to suppress these exponential increases into a linear increase without reducing the classification accuracy.MRCoHOG can reduce the number of dimensions of a feature by calculating the co-occurrence only between adjacent pixels, and it maintains accuracy by extracting features from multiple low-resolution images.We performed classification experiments using a vehicle data set cropped from surveillance images of a parking area and the INRIA Person Data Set, and the results showed that the performance of MRCoHOG is equivalent to that of CoHOG.