Abstract
Recently, car vision technologies have been paid much attention in the field of ITS(Intelligent Transport System). In particular, techniques for automatic detection of humans (or pedestrians) from images have been studied enthusiastically. The HOG feature proposed by Dalal and Triggs is a well known feature for representing and recognizing a human image. This is the reason why the feature is improved by many researchers. However, none of the previous improved techniques are satisfactory in the detection rate and the processing time. In this paper, we propose a method of detecting a human based on the M-HOG (Multiple-HOG) feature and RealAdaBoost using 2D probability density function. The proposed method can optimize the number of histogram's bin and express feature co-occurrence. We also propose a method using Hue (HSV Transform) and M-HOG feature. Experimental results show effectiveness of the proposed method compared to previous ones.
