Design and implementation of a high performance pedestrian detection

Research on pedestrian detection system still presents a lot of space for improvements, both on speed and detection accuracy. This paper presents a full implementation of a pedestrian detection system, using a part-based classification for the candidates identification and a feature based tracking for increasing the result robustness. The novelty of this approach relies on the use of part-based approach with a combination of Haar-cascade and HOG-SVM. Tests have been conducted using standard datasets showing results aligned with those of the other state-of-the-art systems available in literature. Real world tests also show high speed performance.