Disparity statistics for pedestrian detection: combining appearance, motion and stereo

Authors:
Stefan Walk;Konrad Schindler;Bernt Schiele
Affiliations:
Computer Science Department, TU Darmstadt;Computer Science Department, TU Darmstadt and Photogrammetry and Remote Sensing Group, ETH Zürich;Computer Science Department, TU Darmstadt and MPI Informatics, Saarbrücken
Venue:
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
Year:
2010

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Abstract

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.