Co-occurrence Histograms of Oriented Gradients for Human Detection

Abstract

The purpose of the work reported in this paper is to detect humans 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 well-known human detection benchmark datasets: “DaimlerChrysler pedestrian classification benchmark dataset” and “INRIA person data set”. The results show that our method reduces the miss rate by half compared with HOG, and outperforms the state-of-the-art methods on both datasets. Furthermore, as an example of a practical application, we applied our method to a surveillance video eight hours in length. The result shows that our method reduces false positives by half compared with HOG. In addition, CoHOG can be calculated 40% faster than HOG.