Pedestrian Detection Using Multi-Objective Optimization


Pedestrian detection on urban video sequences challenges classification systems because of the presence of cluttered backgrounds which drop their performances. This article proposes a Multi-Objective Optimization (MOO) technique reducing this limitation. It trains a pool of cascades of boosted classifiers using different positive datasets. A Pareto Front is obtained from the locally non-dominated operational points of the Receptive Objective Curve (ROC) of those classifiers. Using information about the dynamic of the scene, different pairs of operational points from the Pareto Front are employed to improve the performance of the system. Results on a real sequences outperform traditional detector systems.