05821275

a

05821275 Walk, Stefan Schindler, Konrad Schiele, Bernt Disparity statistics for pedestrian detection: Combining appearance, motion and stereo. Daniilidis, Kostas (ed.) et al., Computer vision -- ECCV 2010. 11th European conference on computer vision, Heraklion, Crete, Greece, September 5--11, 2010. Proceedings, Part VI. Berlin: Springer (ISBN 978-3-642-15566-6/pbk). Lecture Notes in Computer Science 6316, 182-195 (2010). 2010 Berlin: Springer EN

doi:10.1007/978-3-642-15567-3_14

Summary: Pedestrian detection is an important problem in computer vision due to its importance for applications such as visual surveillance, robotics, and automotive safety. This paper pushes the state-of-the-art of pedestrian detection in two ways. First, we propose a simple yet highly effective novel feature based on binocular disparity, outperforming previously proposed stereo features. Second, we show that the combination of different classifiers often improves performance even when classifiers are based on the same feature or feature combination. These two extensions result in significantly improved performance over the state-of-the-art on two challenging datasets.