Object detection using edge histogram of oriented gradient

In this paper, we address the object detection problem by a proposed gradient feature, the Edge Histogram of Oriented Gradient (Edge-HOG). Edge-HOG consists of several blocks arranged along a line or an arc, which is designed to describe the edge pattern. In addition, we propose a new feature extraction method, which extracts the structural information based on the gravity centers as complementary to traditional gradient histograms. As a result, the proposed Edge-HOG not only reflects the local shape information of objects, but also captures more significant appearance information. Experimental results show that the proposed approach significantly improves both the detection accuracy and the convergence speed compared to the traditional HOG feature. It also achieves performance competitive with some commonly-used methods on pedestrian detection and car detection tasks.