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International Journal of Computer Vision

, Volume 71, Issue 2, pp 143–160 | Cite as

Pedestrian Detection via Periodic Motion Analysis

  • Yang RanEmail author
  • Isaac Weiss
  • Qinfen Zheng
  • Larry S. Davis
Article

Abstract

We describe algorithms for detecting pedestrians in videos acquired by infrared (and color) sensors. Two approaches are proposed based on gait. The first employs computationally efficient periodicity measurements. Unlike other methods, it estimates a periodic motion frequency using two cascading hypothesis testing steps to filter out non-cyclic pixels so that it works well for both radial and lateral walking directions. The extraction of the period is efficient and robust with respect to sensor noise and cluttered background. In order to integrate shape and motion, we convert the cyclic pattern into a binary sequence by Maximal Principal Gait Angle (MPGA) fitting in the second method. It does not require alignment and continuously estimates the period using a Phase-locked Loop. Both methods are evaluated by experimental results that measure performance as a function of size, movement direction, frame rate and sequence length.

Keywords

pedestrian detection periodic motion frequency estimation cyclic gait pattern phase-locked loop 

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Copyright information

© Springer Science + Business Media, LLC 2006

Authors and Affiliations

  • Yang Ran
    • 1
    Email author
  • Isaac Weiss
    • 1
  • Qinfen Zheng
    • 1
  • Larry S. Davis
    • 1
  1. 1.Center for Automation ResearchUniversity of Maryland at College ParkCollege Park

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