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 discussing 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 detection 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.
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CITATION STYLE
Benenson, R., Omran, M., Hosang, J., & Schiele, B. (2015). Ten years of pedestrian detection, what have we learned? In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8926, pp. 613–627). Springer Verlag. https://doi.org/10.1007/978-3-319-16181-5_47
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