Pedestrian detection on CAVIAR dataset using a mo vement feature space
Pablo Negri, Pablo A. Lotito · El Servicio de Difusión de la Creación Intelectual (National University of La Plata) · 2012
Abstract. This work develops a pedestrian detection system using a feature space based on level lines, called Movement Feature Space (MFS). Besides detecting the movement in the scene, this feature space defines the descriptors used by the classifiers to identify pedestrians. Locations hypotheses of pedestrian are performed by a cascade of boosted clas-sifiers. The validation of these regions of interest is carried out by a Support Vector Machine classifier. Results rise to 81 % of good detec-tion rate, having 0.6 false alarms per image on average on the FRONT VIEW CAVIAR dataset.