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Recognizing Constrained 3D Human Motion: An Inference Approach

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Human Motion Sensing and Recognition

Part of the book series: Studies in Computational Intelligence ((SCI,volume 675))

Abstract

Enormous uncertainties in unconstrained human motions lead to a fundamental challenge that many recognising algorithms have to face in practice: motion recognition has to be efficiently correct, but verifying whether or not the algorithm is robustly following the true target motion tends to be demanding, especially when human kinematic motions heavily overlap and occlusion occurs. Due to the lack of a good solution to this problem, many existing methods tend to be either effective but computationally intensive or efficient but vulnerable to false alarms. This chapter presents a novel inference engine for recognising occluded 3D human motion assisted by the recognition context. First, uncertainties are wrapped into a fuzzy membership function via a novel method called fuzzy quantile generation which employs metrics derived from the probabilistic quantile function. Then, time-dependent and context-aware rules are produced via a genetic programming to smooth the qualitative outputs represented by fuzzy membership functions. Finally, occlusion in motion recognition is taken care of by introducing new procedures for feature selection and feature reconstruction. Experimental results demonstrate the effectiveness of the proposed inference engine for 3D occluded human motion recognition.

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Author information

Authors and Affiliations

  1. School of Computing, University of Portsmouth, Portsmouth, UK

    Honghai Liu & Zhaojie Ju

  2. School of Automation, Shenyang Aerospace University, Shenyang, China

    Xiaofei Ji

  3. Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia

    Chee Seng Chan

  4. Mathematics and Physical Sciences, University of Exeter, Exeter, UK

    Mehdi Khoury

Authors
  1. Honghai Liu
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  2. Zhaojie Ju
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  3. Xiaofei Ji
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  4. Chee Seng Chan
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  5. Mehdi Khoury
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Correspondence to Honghai Liu .

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Liu, H., Ju, Z., Ji, X., Chan, C.S., Khoury, M. (2017). Recognizing Constrained 3D Human Motion: An Inference Approach. In: Human Motion Sensing and Recognition. Studies in Computational Intelligence, vol 675. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-53692-6_10

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  • DOI: https://doi.org/10.1007/978-3-662-53692-6_10

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  • Print ISBN: 978-3-662-53690-2

  • Online ISBN: 978-3-662-53692-6

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