Abstract
In the thesis, we propose a method to detect partially occluded pedestrians. The human body can be represented by three part regions—head, torso, and pair of upper legs. Due to highly articulated human poses and varying viewing angles, we segment them according to degree of freedom of human pose space. The head is divided into three positions — middle, right, and left. The torso is divided into two groups — front/back view, and side view. The leg is divided into eight kinds of poses. The human part detector trained using above poses. In feature extraction and classification, we use Histograms of Oriented Gradients (HOG) feature combined with AdaBoost cascade algorithm to train the classifier. HOG feature can successfully capture the contour and edge information. Cascade structure can make the classification more efficient. Part-based detectors have demonstrated their merit in partially occluded human detection. However, there is a key issue to be solved on how to integrate the scores of part detectors. We build occlusion map to find the most likely occlusion type. The highest merging score is the best configuration and reevaluate the detection score of each human. Experimental results on two public datasets (INRIA and Caltech) show the effectiveness of the proposed approach.