Pedestrian Detection by Using FAST-HOG Features

Abstract

Pedestrian detection is used in video surveillance systems and driver assistance systems. The purpose is to build automated vision systems for detecting pedestrians as shown in figure 1. We use Histograms of Oriented Gradients (HOG), which are one of the well-known features for object recognition. HOG features are calculated by taking orientation histograms of edge intensity in a local region [1]. In this paper we select the interesting point in the image by using FAST features detector and extracted HOG features around these strongest corners and use them as an input vector of linear Support Vector Machine (SVM) to classify the given input into pedestrian/non-pedestrian. By using FAST detector we reduce the number of features less than half without lowering the performance.