Co-occurrence Histograms of Oriented Gradients for Pedestrian Detection

This paper proposes a method for extracting feature descriptors consisting of co-occurrence histograms of oriented gradients (CoHOG) that reduces miss rate by half compared with HOG, and outperforms the state-of-the-art methods on both datasets.

Abstract

The purpose of this paper is to detect pedestrians from images. This paper proposes a method for extracting feature descriptors consisting of co-occurrence histograms of oriented gradients (CoHOG). Including co-occurrence with various positional offsets, the feature descriptors can express complex shapes of objects with local and global distributions of gradient orientations. Our method is evaluated with a simple linear classifier on two famous pedestrian detection benchmark datasets: "DaimlerChrysler pedestrian classification benchmark dataset" and "INRIA person data set". The results show that proposed method reduces miss rate by half compared with HOG, and outperforms the state-of-the-art methods on both datasets.