3D-Guided Multiscale Sliding Window for Pedestrian Detection

  • Alejandro González
  • Gabriel Villalonga
  • German Ros
  • David Vázquez
  • Antonio M. López
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9117)


The most relevant modules of a pedestrian detector are the candidate generation and the candidate classification. The former aims at presenting image windows to the latter so that they are classified as containing a pedestrian or not. Much attention has being paid to the classification module, while candidate generation has mainly relied on (multiscale) sliding window pyramid. However, candidate generation is critical for achieving real-time. In this paper we assume a context of autonomous driving based on stereo vision. Accordingly, we evaluate the effect of taking into account the 3D information (derived from the stereo) in order to prune the hundred of thousands windows per image generated by classical pyramidal sliding window. For our study we use a multi-modal (RGB, disparity) and multi-descriptor (HOG, LBP, HOG+LBP) holistic ensemble based on linear SVM. Evaluation on data from the challenging KITTI benchmark suite shows the effectiveness of using 3D information to dramatically reduce the number of candidate windows, even improving the overall pedestrian detection accuracy.


Ground Plane Detection Accuracy Pedestrian Detection Autonomous Driving Image Disparity 
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.



This work is supported by the Spanish MICINN projects TRA2011-29454-C03-01 and TIN2011-29494-C03-02.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Alejandro González
    • 1
  • Gabriel Villalonga
    • 1
  • German Ros
    • 1
  • David Vázquez
    • 1
  • Antonio M. López
    • 1
  1. 1.Computer Vision Center and Universitat Autònoma de BarcelonaBellaterra, BarcelonaSpain

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