Classifying 3D human motions by mixing fuzzy Gaussian inference with genetic programming

Mehdi Khoury; Honghai Liu

doi:10.1007/978-3-642-10817-4_6

Classifying 3D human motions by mixing fuzzy Gaussian inference with genetic programming

Mehdi Khoury^*
, Honghai Liu

^*Corresponding author for this work

University of Portsmouth

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

This paper combines the novel concept of Fuzzy Gaussian Inference(FGI) with Genetic Programming (GP) in order to accurately classify real natural 3d human Motion Capture data. FGI builds Fuzzy Membership Functions that map to hidden Probability Distributions underlying human motions, providing a suitable modelling paradigm for such noisy data. Genetic Programming (GP) is used to make a time dependent and context aware filter that improves the qualitative output of the classifier. Results show that FGI outperforms a GMM-based classifier when recognizing seven different boxing stances simultaneously, and that the addition of the GP based filter improves the accuracy of the FGI classifier significantly.

Original language	English
Title of host publication	Intelligent Robotics and Applications - Second International Conference, ICIRA 2009, Proceedings
Pages	55-66
Number of pages	12
DOIs	https://doi.org/10.1007/978-3-642-10817-4_6
State	Published - 2009
Externally published	Yes
Event	2nd International Conference on Intelligent Robotics and Applications, ICIRA 2009 - Singapore, Singapore Duration: 16 Dec 2009 → 18 Dec 2009

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	5928 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	2nd International Conference on Intelligent Robotics and Applications, ICIRA 2009
Country/Territory	Singapore
City	Singapore
Period	16/12/09 → 18/12/09

Access to Document

10.1007/978-3-642-10817-4_6

Cite this

Khoury, M., & Liu, H. (2009). Classifying 3D human motions by mixing fuzzy Gaussian inference with genetic programming. In Intelligent Robotics and Applications - Second International Conference, ICIRA 2009, Proceedings (pp. 55-66). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5928 LNAI). https://doi.org/10.1007/978-3-642-10817-4_6

Khoury, Mehdi ; Liu, Honghai. / Classifying 3D human motions by mixing fuzzy Gaussian inference with genetic programming. Intelligent Robotics and Applications - Second International Conference, ICIRA 2009, Proceedings. 2009. pp. 55-66 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{5c99b18f90ad4406b40e76b2e6a01599,

title = "Classifying 3D human motions by mixing fuzzy Gaussian inference with genetic programming",

abstract = "This paper combines the novel concept of Fuzzy Gaussian Inference(FGI) with Genetic Programming (GP) in order to accurately classify real natural 3d human Motion Capture data. FGI builds Fuzzy Membership Functions that map to hidden Probability Distributions underlying human motions, providing a suitable modelling paradigm for such noisy data. Genetic Programming (GP) is used to make a time dependent and context aware filter that improves the qualitative output of the classifier. Results show that FGI outperforms a GMM-based classifier when recognizing seven different boxing stances simultaneously, and that the addition of the GP based filter improves the accuracy of the FGI classifier significantly.",

author = "Mehdi Khoury and Honghai Liu",

year = "2009",

doi = "10.1007/978-3-642-10817-4\_6",

language = "英语",

isbn = "3642108164",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

pages = "55--66",

booktitle = "Intelligent Robotics and Applications - Second International Conference, ICIRA 2009, Proceedings",

note = "2nd International Conference on Intelligent Robotics and Applications, ICIRA 2009 ; Conference date: 16-12-2009 Through 18-12-2009",

}

Khoury, M & Liu, H 2009, Classifying 3D human motions by mixing fuzzy Gaussian inference with genetic programming. in Intelligent Robotics and Applications - Second International Conference, ICIRA 2009, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5928 LNAI, pp. 55-66, 2nd International Conference on Intelligent Robotics and Applications, ICIRA 2009, Singapore, Singapore, 16/12/09. https://doi.org/10.1007/978-3-642-10817-4_6

Classifying 3D human motions by mixing fuzzy Gaussian inference with genetic programming. / Khoury, Mehdi; Liu, Honghai.
Intelligent Robotics and Applications - Second International Conference, ICIRA 2009, Proceedings. 2009. p. 55-66 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5928 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - This paper combines the novel concept of Fuzzy Gaussian Inference(FGI) with Genetic Programming (GP) in order to accurately classify real natural 3d human Motion Capture data. FGI builds Fuzzy Membership Functions that map to hidden Probability Distributions underlying human motions, providing a suitable modelling paradigm for such noisy data. Genetic Programming (GP) is used to make a time dependent and context aware filter that improves the qualitative output of the classifier. Results show that FGI outperforms a GMM-based classifier when recognizing seven different boxing stances simultaneously, and that the addition of the GP based filter improves the accuracy of the FGI classifier significantly.

AB - This paper combines the novel concept of Fuzzy Gaussian Inference(FGI) with Genetic Programming (GP) in order to accurately classify real natural 3d human Motion Capture data. FGI builds Fuzzy Membership Functions that map to hidden Probability Distributions underlying human motions, providing a suitable modelling paradigm for such noisy data. Genetic Programming (GP) is used to make a time dependent and context aware filter that improves the qualitative output of the classifier. Results show that FGI outperforms a GMM-based classifier when recognizing seven different boxing stances simultaneously, and that the addition of the GP based filter improves the accuracy of the FGI classifier significantly.

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Khoury M, Liu H. Classifying 3D human motions by mixing fuzzy Gaussian inference with genetic programming. In Intelligent Robotics and Applications - Second International Conference, ICIRA 2009, Proceedings. 2009. p. 55-66. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-10817-4_6

Classifying 3D human motions by mixing fuzzy Gaussian inference with genetic programming

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