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Human Motion Tracking by Combining View-Based and Model-Based Methods for Monocular Video Sequences

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Computational Science and Its Applications — ICCSA 2003 (ICCSA 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2669))

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Abstract

Reliable tracking of moving humans is essential to motion estimation, video surveillance and human-computer interface. This paper presents a new approach to human motion tracking that combines view-based and model-based techniques. Monocular color video is processed at both pixel level and object level. At the pixel level, a Gaussian mixture model is used to train and classify individual pixel colors. At the object level, a 3D human body model projected on a 2D image plane is used to fit the image data. Our method does not use inverse kinematics due to the singularity problem. While many others use stochastic sampling for model-based motion tracking, our method is purely dependent on parameter optimization. We convert the human motion tracking problem into a parameter optimization problem. A cost function for parameter optimization is used to estimate the degree of the overlapping between the foreground input image silhouette and a projected 3D model body silhouette. The overlapping is computed using computational geometry by converting a set of pixels from the image domain to a polygon in the real projection plane domain. Our method is used to recognize various human motions. Motion tracking results from video sequences are very encouraging.

This work was partially supported by grant No. 2000-2-30400-011-1 from the Korea Science and Engineering Foundation. We thank Ms. Debi Prather for proofreading of this paper.

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Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, 78712

    Jihun Park, Sangho Park & J. K. Aggarwal

  2. Department of Computer Engineering, Hongik University, Seoul, Korea

    Jihun Park, Sangho Park & J. K. Aggarwal

  3. Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, 78712

    Jihun Park, Sangho Park & J. K. Aggarwal

Authors
  1. Jihun Park
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  2. Sangho Park
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  3. J. K. Aggarwal
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Editor information

Editors and Affiliations

  1. Army High Performance Computing Research Center, USA University of Minessota Department of Computer Science and Engineering, MN, 55455, USA

    Vipin Kumar

  2. Department of Computer Science, University of Calgary, Calgary, AB, T2N1N4, Canada

    Marina L. Gavrilova

  3. Heuchera Technologies Inc., 122 9251-8Yonge Street, Richmond Hill, ON, Canada, L4C 9T3

    Chih Jeng Kenneth Tan

  4. School of Computer Science, The Queen’s University of Belfast, Belfast, BT7 1NN, Northern Ireland UK

    Chih Jeng Kenneth Tan

  5. Département d’informatique et de recherche opérationelle Montréal, Université de Montréal, Québec, H3C 3J7, Canada

    Pierre L’Ecuyer

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© 2003 Springer-Verlag Berlin Heidelberg

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Park, J., Park, S., Aggarwal, J.K. (2003). Human Motion Tracking by Combining View-Based and Model-Based Methods for Monocular Video Sequences. In: Kumar, V., Gavrilova, M.L., Tan, C.J.K., L’Ecuyer, P. (eds) Computational Science and Its Applications — ICCSA 2003. ICCSA 2003. Lecture Notes in Computer Science, vol 2669. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44842-X_66

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  • DOI: https://doi.org/10.1007/3-540-44842-X_66

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40156-8

  • Online ISBN: 978-3-540-44842-6

  • eBook Packages: Springer Book Archive

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