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Combining Template Matching and Model Fitting for Human Body Segmentation and Tracking with Applications to Sports Training

  • Hao-Jie Li
  • Shou-Xun Lin
  • Yong-Dong Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4142)

Abstract

This paper present a method for extracting and automatic tracking of human body using template matching and human body model fitting for specific activity. The method includes training and testing stages. For training, the body shapes are manually segmented from image sequences as templates and are clustered. The 2D joint locations of each cluster center are labeled and the dynamical models of the templates are learned. For testing, a “seed” frame is first selected from the sequence according to the reliability of motion segmentation and several most matched templates to it are obtained. Then, a template tracking process within a probabilistic framework integrating the learnt dynamical model is started forwards and afterwards until the entire sequence is matched. Thirdly, a articulated 2D human body model is initialized from the matched template and then iteratively fit to the image features. Thus, the human body segmentation results and 2D body joints are got. Experiments are performed on broadcasted diving sequences and promising results are obtained. We also demonstrate two applications of the proposed method for sports training.

Keywords

Template Match Motion Segmentation Knee Joint Angle Human Body Model Neighboring Frame 
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 2006

Authors and Affiliations

  • Hao-Jie Li
    • 1
    • 2
  • Shou-Xun Lin
    • 1
  • Yong-Dong Zhang
    • 1
  1. 1.Institute of Computing TechnologyChinese Academy of SciencesBeijingChina
  2. 2.Graduate School of the Chinese Academy of SciencesBeijingChina

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