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ECCV
2010
Springer

Disparity Statistics for Pedestrian Detection: Combining Appearance, Motion and Stereo

3 years 8 months ago
Disparity Statistics for Pedestrian Detection: Combining Appearance, Motion and Stereo
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.
Stefan Walk, Konrad Schindler, Bernt Schiele
Added 25 Jan 2011
Updated 25 Jan 2011
Type Journal
Year 2010
Where ECCV
Authors Stefan Walk, Konrad Schindler, Bernt Schiele
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