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Robust Object Tracking Algorithm in Natural Environments

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Advances in Natural Computation (ICNC 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4222))

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Abstract

In order to realize robust visual tracking in natural environments, a novel algorithm based on adaptive appearance model is proposed. The model can adapt to changes in object appearance over time. A mixture of three Gaussian distributions models the value of each pixel. An online Expectation Maximization (EM) algorithm is developed to update the parameters of the Gaussians. The observation model in the particle filter is designed based on the adaptive appearance model. Numerous experimental results demonstrate that our proposed algorithm can track objects well under illumination change, large pose variation, and partial or full occlusion.

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Author information

Authors and Affiliations

  1. College of Informatics and Electronics, Hebei University of Science and Technology, Shijiazhuang, 050054, China

    Shi-qiang Hu & Guo-zhuang Liang

  2. Institute of Informatics and Electronics, Shanghai Jiaotong University, Shanghai, 200030, China

    Zhong-liang Jing

Authors
  1. Shi-qiang Hu
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  2. Guo-zhuang Liang
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  3. Zhong-liang Jing
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Editor information

Editors and Affiliations

  1. Life Science Research Center, School of Electronic Engineering, Xidian University, 710071, Xi’an, Shaanxi, China

    Licheng Jiao

  2. School of Electrical and Electronic Engineering, Nanyang Technological University, Block S1, Nanyang Avenue, 639798, Singapore

    Lipo Wang

  3. School of Electronic Engineering, Xidian Univ., P.O. Box, 710071, Xi’an, P.R. China

    Xinbo Gao

  4. College of Mathematics and Information Science, Hebei Normal University, 050016, Shijiazhuang, Hebei, P.R. China

    Jing Liu

  5. Multi-Agent Systems Lab,Department of Computer Science, University of Science and Technology of China, 230026, Hefei, China

    Feng Wu

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© 2006 Springer-Verlag Berlin Heidelberg

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Hu, Sq., Liang, Gz., Jing, Zl. (2006). Robust Object Tracking Algorithm in Natural Environments. In: Jiao, L., Wang, L., Gao, X., Liu, J., Wu, F. (eds) Advances in Natural Computation. ICNC 2006. Lecture Notes in Computer Science, vol 4222. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881223_64

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  • DOI: https://doi.org/10.1007/11881223_64

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-45907-1

  • Online ISBN: 978-3-540-45909-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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