Speeding Up HOG and LBP Features for Pedestrian Detection by Multiresolution Techniques

  • Philip Geismann
  • Alois Knoll
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6453)


In this article, we present a fast pedestrian detection system for driving assistance. We use current state-of-the-art HOG and LBP features and combine them into a set of powerful classifiers. We propose an encoding scheme that enables LBP to be used efficiently with the integral image approach. This way, HOG and LBP block features can be computed in constant time, regardless of block position or scale. To further speed up the detection process, a coarse-to-fine scanning strategy based on input resolution is employed. The original camera resolution is consecutively downsampled and fed to different stage classifiers. Early stages in low resolutions reject most of the negative candidate regions, while few samples are passed through all stages and are evaluated by more complex features. Results presented on the INRIA set show competetive accuracy performance, while both processing and training time of our system outperforms current state-of-the-art work.


Local Binary Pattern Integral Image Pedestrian Detection Local Binary Pattern Feature Integral Histogram 
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

  • Philip Geismann
    • 1
  • Alois Knoll
    • 1
  1. 1.Robotics and Embedded SystemsTechnische Universität MünchenGarchingGermany

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