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Optimized Particles for 3-D Tracking

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Intelligent Robotics and Applications (ICIRA 2010)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6424))

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Abstract

3-D visual tracking is useful for many of its applications. In this paper, we propose two different ways for different system configurations to optimize particle filter for enhancing 3-D tracking performances. On one hand, a new data fusion method is proposed to obtain the optimal importance density function for active vision systems. On the other hand, we develop a method for reconfigurable vision systems to maximize the effective sampling size in particle filter, which consequentially helps to solve the degeneracy problem and minimize the tracking error.

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Author information

Authors and Affiliations

  1. Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong

    Huiying Chen

  2. Department of Manufacturing Engineering and Engineering Management, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong

    Youfu Li

Authors
  1. Huiying Chen
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  2. Youfu Li
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Editor information

Editors and Affiliations

  1. School of Creative Technologies, The University of Portsmouth, PO1 2DJ, Portsmouth, UK

    Honghai Liu

  2. Robotics Institute, Shanghai Jiao Tong University, 200240, Shanghai, China

    Han Ding , Zhenhua Xiong  & Xiangyang Zhu ,  & 

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© 2010 Springer-Verlag Berlin Heidelberg

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Chen, H., Li, Y. (2010). Optimized Particles for 3-D Tracking. In: Liu, H., Ding, H., Xiong, Z., Zhu, X. (eds) Intelligent Robotics and Applications. ICIRA 2010. Lecture Notes in Computer Science(), vol 6424. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16584-9_71

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  • DOI: https://doi.org/10.1007/978-3-642-16584-9_71

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-16583-2

  • Online ISBN: 978-3-642-16584-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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