Pedestrian Detection using a boosted cascade of Histogram of Oriented Gradients

Pedestrian detection has been an active area of research in recent years; its interest relies on the potential positive impact on quality of life of the related applications (surveillance systems, automotive safety, robotics, multimedia content analysis, assistive technology and advanced interactive interfaces, among others). The large variability of human appearances, poses and context conditions makes pedestrian detection to be one of the most challenging tasks in computer vision. Although many significant approaches have been proposed lately, pedestrian detection still offers a wide framework of improvement, mainly in terms of accuracy and efficiency. The present thesis aims to study the influence of different training parameters values on the performance of a pedestrian detector. First, a pedestrian detector, using a boosted cascade of Histograms of Oriented Gradients, is built from scratch. Afterwards, a sensitivity analysis is carried out taking into account significant variables, such as the number of training samples, the feature (HOG) or classifier (SVM) parameters, the feature selection technique, the negatives resampling treatment and the typology of the employed weak classifier.