Partitioning Gait Cycles Adaptive to Fluctuating Periods and Bad Silhouettes

  • Jianyi Liu
  • Nanning Zheng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4642)


Period detection and cycle partitioning are always the very beginning for most gait recognition algorithms. Badly segmented silhouettes and random fluctuations in walking speed are two of the main problems for this basic but important issue. In this paper, we propose a method of cycle partitioning that is adaptive to silhouette quality and speed fluctuations. To do that, autocorrelation on sliding window is proposed to quantify the silhouette quality into “trusted zones” and “uncertain zones”. Prior period estimation and observation of fluctuations are incorporated to obtain more precise cycle detection. One criterion based on the difference of Common Phase Frames (CPF) is proposed to evaluate the precision of detection. In experiment, our method was compared with the traditional autocorrelation method using sequences from the USF gait database. The results showed the improved cycle partitioning performance of the proposed method.


Gait Cycle Gesture Recognition Dynamic Time Warping Gait Recognition Automatic Face 
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 2007

Authors and Affiliations

  • Jianyi Liu
    • 1
  • Nanning Zheng
    • 1
  1. 1.Institute of AI & Robotics, Xi’an Jiaotong UniversityP.R. China

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