Applying HOG feature to the detection and tracking of a human on a bicycle

Detection of a human on a bicycle is an important research subject in an advanced safety vehicle driving system to decrease traffic accidents. The Histograms of Oriented Gradients (HOG) feature has been proposed as useful feature for detecting a standing human in various kinds of background. So, many researchers use currently the HOG feature to detect a human. Detecting a human on a bicycle is more difficult than detecting a human because a bicycle´s appearance can change dramatically according to viewpoints. In this paper, we propose a method of detecting a human on a bicycle using HOG feature and RealAdaboost algorithm. When detecting a human on a bicycle, occlusion is a cause of decreasing detection efficiency. Occlusion is a serious matter in car vision research because there are occlusions in real transportation environment. We decide the next position of a human on a bicycle using object tracking. Experimental results and evaluation show satisfactory performance of the proposed method.