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Nonlinear Background Filter to Improve Pedestrian Detection

  • Yi WangEmail author
  • Sébastien Piérard
  • Song-Zhi Su
  • Pierre-Marc Jodoin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9281)

Abstract

In this paper, we propose a simple nonlinear filter which improves the detection of pedestrians walking in a video. We do so by first cumulating temporal gradient of moving objects into a motion history image (MHI). Then we apply to each frame of the video a motion-guided nonlinear filter whose goal is to smudge out background details while leaving untouched foreground moving objects. The resulting blurry-background image is then fed to a pedestrian detector. Experiments reveal that for a given miss rate, our motion-guided nonlinear filter can decrease the number of false positives per image (FPPI) by a factor of up to 26. Our method is simple, computationally light, and can be applied on a variety of videos to improve the performances of almost any kind of pedestrian detectors.

Keywords

Motion detection Pedestrian detection Motion history image Nonlinear filtering 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Yi Wang
    • 1
    Email author
  • Sébastien Piérard
    • 2
  • Song-Zhi Su
    • 3
  • Pierre-Marc Jodoin
    • 1
  1. 1.Department of Computer ScienceUniversity of SherbrookeSherbrookeCanada
  2. 2.INTELSIG Laboratory, Montefiore InstituteUniversity of LiègeLiègeBelgium
  3. 3.School of Information Science and TechnologyXiamen UniversityXiamenChina

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