Reconstruction of 3D Human Body Pose for Gait Recognition

  • Hee-Deok Yang
  • Seong-Whan Lee
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3832)

Abstract

In this paper, we propose a novel method to reconstruct 3D human body pose for gait recognition from monocular image sequences based on top-down learning. Human body pose is represented by a linear combination of prototypes of 2D silhouette images and their corresponding 3D body models in terms of the position of a predetermined set of joints. With a 2D silhouette image, we can estimate optimal coefficients for a linear combination of prototypes of the 2D silhouette images by solving least square minimization. The 3D body model of the input silhouette image is obtained by applying the estimated coefficients to the corresponding 3D body model of prototypes. In the learning stage, the proposed method is hierarchically constructed by classifying the training data into several clusters recursively. Also, in the reconstructing stage, the proposed method hierarchically reconstructs 3D human body pose with a silhouette image. The experimental results show that our method can be efficient and effective to reconstruct 3D human body pose for gait recognition.

Keywords

Gait Recognition Silhouette Image Gait Representation Monocular Image Sequence Gesture Database 
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 2005

Authors and Affiliations

  • Hee-Deok Yang
    • 1
  • Seong-Whan Lee
    • 1
  1. 1.Department of Computer Science and EngineeringKorea UniversitySeoulKorea

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