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Study over Cerebellum Prediction Model During Hand Tracking

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Geo-Spatial Knowledge and Intelligence (GRMSE 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 698))

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Abstract

This paper adopted a new particle filter method to reduce the dimension of particle sampling during hand tracking and describes the posterior probability distribution of state variable with few particles. The manuscript presents three core issues: firstly, we studied the characteristics of relevant kinetics during the hand tracking and the operator’s cognitive psychology features under the man-machine interaction condition, and established a cerebellum prediction model by analyzing the operator’s behavioral characteristics during hand tracking; secondly, we studied the tracking algorithms related to the cerebellum model built; thirdly, we made a comparison with traditional particle filter algorithm through simulation. As shown in experimental results, the proposed algorithm in this paper can significantly improve both the tracking speed and precision.

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Acknowledgment

This paper is subsidized by the National Science Foundation (61271334) & (61373065).

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Authors and Affiliations

  1. College of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing, 210046, China

    Shaobai Zhang & Qun Chen

Authors
  1. Shaobai Zhang
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  2. Qun Chen
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Corresponding author

Correspondence to Shaobai Zhang .

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Editors and Affiliations

  1. Beijing Institute of Technology, Beijing, China

    Hanning Yuan

  2. Beijing Institute of Technology, Beijing, China

    Jing Geng

  3. Wuhan University, Wuhan, China

    Fuling Bian

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Zhang, S., Chen, Q. (2017). Study over Cerebellum Prediction Model During Hand Tracking. In: Yuan, H., Geng, J., Bian, F. (eds) Geo-Spatial Knowledge and Intelligence. GRMSE 2016. Communications in Computer and Information Science, vol 698. Springer, Singapore. https://doi.org/10.1007/978-981-10-3966-9_17

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  • DOI: https://doi.org/10.1007/978-981-10-3966-9_17

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-3965-2

  • Online ISBN: 978-981-10-3966-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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