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

  • Stefan Walk
  • Konrad Schindler
  • Bernt Schiele
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6316)

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.

Keywords

Ground Plane Motion Information Human Detection Pedestrian Detection Disparity Statistics 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Stefan Walk
    • 1
  • Konrad Schindler
    • 1
    • 2
  • Bernt Schiele
    • 1
    • 3
  1. 1.Computer Science DepartmentTU Darmstadt 
  2. 2.Photogrammetry and Remote Sensing GroupETH Zürich 
  3. 3.MPI InformaticsSaarbrücken

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