Evolutionary joint selection to improve human action recognition with RGB-D devices |
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| Affiliation: | 1. Department of Computer Technology, University of Alicante, P.O. Box 99, E-03080 Alicante, Spain;2. Faculty of Science, Engineering and Computing, Kingston University, Penrhyn Road, KT1 2EE Kingston upon Thames, United Kingdom;1. Research Program of Applied Mathematics and Computations, Mexican Petroleum Institute;2. Graduate Programs on Computer Sciences Tecnologico de Monterrey, Campus Estado de México;1. Tecnológico de Monterrey, Campus Estado de México, Carretera Lago de Guadalupe Km 3.5, Atizapán de Zaragoza, Estado de México C.P. 52926, Mexico;2. Universidad Politécnica de Chiapas, Eduardo J. Selvas s/n, Tuxtla Gutiérrez, Chiapas, Mexico;1. IRTES-SeT, University of Technology Belfort-Montbeliard, 13 rue Ernest-Thierry Mieg, 90010 Belfort cedex, France;2. CETE SO, Center for Technical Studies of South West, 1 Avenue du Colonel Roche, 31400 Toulouse, France |
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| Abstract: | Interest in RGB-D devices is increasing due to their low price and the wide range of possible applications that come along. These devices provide a marker-less body pose estimation by means of skeletal data consisting of 3D positions of body joints. These can be further used for pose, gesture or action recognition. In this work, an evolutionary algorithm is used to determine the optimal subset of skeleton joints, taking into account the topological structure of the skeleton, in order to improve the final success rate. The proposed method has been validated using a state-of-the-art RGB action recognition approach, and applying it to the MSR-Action3D dataset. Results show that the proposed algorithm is able to significantly improve the initial recognition rate and to yield similar or better success rates than the state-of-the-art methods. |
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| Keywords: | RGB-D devices Human action recognition Evolutionary computation Instance selection Feature subset selection |
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