Pedestrian Detection Under Partial Occlusion by using Logic Inference, HOG and SVM

This article presents an algorithm for the detection of pedestrians in urban driving environments during the day. The main contribution is in the design of a new classifier to discriminate between the person and the background, under partial occlusion. To construct the classifier, the HOG (Histogram of Oriented Gradients) descriptor was used together with the SVM (Support Vector Machine) and IL (Logic Inference) algorithms.The input image has been divided into twelve regions, and for each of them the feature vector has been extracted and a classifier based on SVM has been built. With this design it is possible to capture the specific detail of each part of the human body, such as head, legs, arms and body. Subsequently, they have been joined in a final classifier using IL, in order to obtain an efficient algorithm to discriminate between partially occluded pedestrians and the background, in urban environments during the day. The experiments related to the classifier were developed onseveral public databases, in various degrees of partial occlusion; and the experiments linked to the detection were generated on the visual information obtained by the experimental platform ViiA, to validate the proposal under real driving conditions.