Abstract
This paper presents a feature selection comparison oriented to human action recognition only with the kinematic features of skeleton representation. For this purpose, three relevance methods are used to rank the contribution of kinematic features for classifying an action is employed. Particularly, the method with the best results includes the supervised information regarding the action to find out a relevant set of features, encoding the most discriminative information. Experimental results are obtained on a well-known public data (MSR Action3D). Results are encouraging to use kernel theory methods to get better kinematic feature selection for each action with a good generalization indistinct to the subject.
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Acknowledgments
This work is supported by the project number 36075 funded by Universidad Nacional de Colombia sede Manizales, by program “Doctorados Nacionales 2014” number 647 funded by COLCIENCIAS, as well as partial Ph.D. financial support from Universidad Autonoma de Occidente.
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Pulgarin-Giraldo, J.D., Ruales-Torres, A.A., Alvarez-Meza, A.M., Castellanos-Dominguez, G. (2017). Relevant Kinematic Feature Selection to Support Human Action Recognition in MoCap Data. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo Moreo, J., Adeli, H. (eds) Biomedical Applications Based on Natural and Artificial Computing. IWINAC 2017. Lecture Notes in Computer Science(), vol 10338. Springer, Cham. https://doi.org/10.1007/978-3-319-59773-7_51
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DOI: https://doi.org/10.1007/978-3-319-59773-7_51
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