Ten Years of Pedestrian Detection, What Have We Learned? Rodrigo Benenson Mohamed Omran Jan Hosang Bernt Schiele Max Planck Institut for Informatics Saarbrücken, Germany firstname.lastname@mpi-inf.mpg.de Abstract Paper-by-paper results make it easy to miss the forest for the trees.We analyse the remarkable progress of the last decade by dis- cussing the main ideas explored in the 40+ detectors currently present in the Caltech pedestrian detection benchmark. We observe that there exist three families of approaches, all currently reaching similar detec- tion quality. Based on our analysis, we study the complementarity of the most promising ideas by combining multiple published strategies. This new decision forest detector achieves the current best known performance on the challenging Caltech-USA dataset. 1 Introduction 10 −3 10 −2 10 −1 10 0 10 1 2004 2014 Caltech-USA reasonable set false positives per image m is s ra te .05 .10 .20 .30 .40 .50
