Abstract
In this paper we present a technique to detect pedestrian. Histogram of gradients (HOG) and Haar wavelets with the aid of support vector machines (SVM) and Ada Boost classifiers show good identification performance on different objects classification including pedestrians. We propose a new shape descriptor derived from the intra-relationship between gradient orientations in a way similar to the HOG. The proposed descriptor is a two 2-D grid of orientation similarities measured at different offsets. The gradient magnitudes and phases derived from a sliding window with different scales and sizes are used to construct two 2-D symmetric grids. The first grid measures the co-occurence of the phases while the other one measures the corresponding percentage of gradient magnitudes for the measured orientation similarity. Since the resultant matrices will be symmetric, the feature vector is formed by concatenating the upper diagonal grid coefficients collected in a raster way. Classification is done using SVM classifier with radial basis kernel. Experimental results show improved performance compared to the current state-of-art techniques.