Object detection using Edge Direction Histogram features

In In this study, we propose an object detection approach using edge direction histogram features. Since edge points are related to shape information closely. Edge Direction Histogram (EDH) is a very simple and direct way to characterize shape information of an object. We divide an object into several parts and employ edge direction histogram method to extract the EDH features. The EDH descriptor is designed to decouple variations of the object due to affine warps and other forms of shape deformations. We further train a support vector machine classifier for each object part and apply a generalized Hough voting scheme to generate object locations and scales. We evaluate the proposed approach on two different kinds of objects: Car and h. Experimental results show that the proposed approach is efficient and robust in object detection. © 2013 Asian Network for Scientific Information.