Classifying 3D Human Motions by Mixing Fuzzy Gaussian Inference with Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7917

@InProceedings{conf/icira/KhouryL09,

• title = "Classifying {3D} Human Motions by Mixing Fuzzy Gaussian Inference with Genetic Programming",
• author = "Mehdi Khoury and Honghai Liu",
• booktitle = "Second International Conference on Intelligent Robotics and Applications, ICIRA 2009",
• editor = "Ming Xie and Youlun Xiong and Caihua Xiong and Honghai Liu and Zhencheng Hu",
• year = "2009",
• volume = "5928",
• series = "Lecture Notes in Computer Science",
• pages = "55--66",
• address = "Singapore",
• month = dec # " 16-18",
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-3-642-10816-7",
• DOI = "doi:10.1007/978-3-642-10817-4_6",
• bibdate = "2009-12-18",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/icira/icira2009.html#KhouryL09",
• 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.",

}

Genetic Programming entries for Mehdi Khoury Honghai Liu

Citations