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3D human motion tracking by using interactive multiple models

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Abstract

Of different model-based methods in vision based human tracking, many state of the art works focus on the stochastic optimization method to search in a very high dimensional space and try to find the optimal solution according to a proper likelihood function. Seldom works perform a framework of interactive multiple models (IMM) to track a human for challenging problems, such as uncertainty of motion styles, imprecise detection of feature points and ambiguity of joint location. This paper presents a two-layer filter framework based on IMM to track human motion. First, a method of model based points location is proposed to detect key feature points automatically and the filter in the first layer is performed to estimate the undetected points. Second, multiple models of motion are learned by the prior motion data with ridge regression and the IMM algorithm is used to estimate the quaternion vectors of joints rotation. Finally, experiments using real images sequences, simulation videos and 3D voxel data demonstrate that this human tracking framework is efficient.

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Authors and Affiliations

  1. School of Computer and Information, Shanghai University of Electric Power, Shanghai, 200090, China

    Ming-lei Tong  (仝明磊) & Hou-qin Bian  (边后琴)

Authors
  1. Ming-lei Tong  (仝明磊)
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  2. Hou-qin Bian  (边后琴)
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Corresponding author

Correspondence to Ming-lei Tong  (仝明磊).

Additional information

Foundation item: the Research Fund for the Young Teacher of Shanghai (No. Z-2009-12) and the New Teacher Fund of Shanghai University of Electric Power (No. K-2010-16)

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Tong, Ml., Bian, Hq. 3D human motion tracking by using interactive multiple models. J. Shanghai Jiaotong Univ. (Sci.) 16, 420–428 (2011). https://doi.org/10.1007/s12204-011-1134-3

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  • DOI: https://doi.org/10.1007/s12204-011-1134-3

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