An improved pedestrian detection algorithm integrating haar-like features and HOG descriptors

Abstract

Considering the importance of pedestrian detection in a variety of applications such as advanced robots and intelligent surveillance systems, this paper presents an improved pedestrian detection method through integrating Haar-like features, AdaBoost algorithm, histogram of oriented gradients (HOG) descriptor, and support vector machine (SVM) classifiers, in which the head and shoulder information is utilized especially. Due to the fast training speed of Haar-like features and the high detection efficiency of HOG features, the proposed method can classify pedestrians precisely with higher speed. Experimental results validated the efficiency and effectiveness of the proposed algorithm. © 2013 Yun Wei et al.