{"title":"Relevant LMA Features for Human Motion Recognition","authors":"Insaf Ajili, Malik Mallem, Jean-Yves Didier","volume":141,"journal":"International Journal of Computer and Information Engineering","pagesStart":792,"pagesEnd":797,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/10009574","abstract":"Motion recognition from videos is actually a very<br \/>\r\ncomplex task due to the high variability of motions. This paper<br \/>\r\ndescribes the challenges of human motion recognition, especially<br \/>\r\nmotion representation step with relevant features. Our descriptor<br \/>\r\nvector is inspired from Laban Movement Analysis method. We<br \/>\r\npropose discriminative features using the Random Forest algorithm<br \/>\r\nin order to remove redundant features and make learning algorithms<br \/>\r\noperate faster and more effectively. We validate our method on<br \/>\r\nMSRC-12 and UTKinect datasets.","references":"1] I. Ajili, M. Mallem, and J. Y. Didier. Gesture recognition for humanoid\r\nrobot teleoperation. In 2017 26th IEEE International Symposium\r\non Robot and Human Interactive Communication (RO-MAN), pages\r\n1115\u20131120, Aug 2017.\r\n[2] I. Ajili, M. Mallem, and J.-Y. Didier. Robust human action\r\nrecognition system using laban movement analysis. Procedia Computer\r\nScience, 112(Supplement C):554 \u2013 563, 2017. Knowledge-Based and\r\nIntelligent Information & Engineering Systems: Proceedings of the\r\n21st International Conference, KES-20176-8 September 2017, Marseille,\r\nFrance.\r\n[3] C. B. Barber, D. P. Dobkin, and H. Huhdanpaa. The quickhull algorithm\r\nfor convex hulls. ACM Trans. Math. Softw., 22(4):469\u2013483, Dec. 1996.\r\n[4] L. Breiman. Random forests. Mach. Learn., 45(1):5\u201332, Oct. 2001.\r\n[5] A. B.Surendiran1. Feature selection using stepwise anova discriminant\r\nanalysis for mammogram mass classification. International Journal on\r\nSignal & Image Processing, 2(1):4, January 2011.\r\n[6] S. Fothergill, H. Mentis, P. Kohli, and S. Nowozin. Instructing people\r\nfor training gestural interactive systems. In Proceedings of the SIGCHI\r\nConference on Human Factors in Computing Systems, CHI \u201912, pages\r\n1737\u20131746, New York, NY, USA, 2012. ACM.\r\n[7] M. E. Hussein, M. Torki, M. A. Gowayyed, and M. El-Saban. Human\r\naction recognition using a temporal hierarchy of covariance descriptors\r\non 3d joint locations. In Proceedings of the Twenty-Third International\r\nJoint Conference on Artificial Intelligence, IJCAI \u201913, pages 2466\u20132472.\r\nAAAI Press, 2013.\r\n[8] I. Laptev and T. Lindeberg. Space-time interest points. In Computer\r\nVision, 2003. Proceedings. Ninth IEEE International Conference on,\r\npages 432\u2013439. IEEE, 2003.\r\n[9] C. Lazar, J. Taminau, S. Meganck, D. Steenhoff, A. Coletta, C. Molter,\r\nV. de Schaetzen, R. Duque, H. Bersini, and A. Nowe. A survey on filter\r\ntechniques for feature selection in gene expression microarray analysis.\r\nIEEE\/ACM Trans. Comput. Biol. Bioinformatics, 9(4):1106\u20131119, July\r\n2012.\r\n[10] A. M. Lehrmann, P. V. Gehler, and S. Nowozin. Efficient nonlinear\r\nmarkov models for human motion. In 2014 IEEE Conference on\r\nComputer Vision and Pattern Recognition, pages 1314\u20131321, June 2014.\r\n[11] H. Wang, A. Kl\u00a8aser, C. Schmid, and C.-L. Liu. Dense trajectories and\r\nmotion boundary descriptors for action recognition. Int. J. Comput. Vis.,\r\n103(1):60\u201379, 2013.\r\n[12] P. Wang, Z. Li, Y. Hou, and W. Li. Action recognition based on\r\njoint trajectory maps using convolutional neural networks. CoRR,\r\nabs\/1611.02447, 2016.\r\n[13] J. Weston, S. Mukherjee, O. Chapelle, M. Pontil, T. Poggio, and\r\nV. Vapnik. Feature selection for svms. In Proceedings of the 13th\r\nInternational Conference on Neural Information Processing Systems,\r\nNIPS\u201900, pages 647\u2013653, Cambridge, MA, USA, 2000. MIT Press.\r\n[14] L. Xia, C. C. Chen, and J. K. Aggarwal. View invariant human action\r\nrecognition using histograms of 3d joints. In 2012 IEEE Computer\r\nSociety Conference on Computer Vision and Pattern Recognition\r\nWorkshops, pages 20\u201327, June 2012.\r\n[15] M. Yamada, W. Jitkrittum, L. Sigal, E. P. Xing, and M. Sugiyama.\r\nHigh-dimensional feature selection by feature-wise non-linear lasso.\r\nArXiv e-prints, Feb. 2012.\r\n[16] L. Zhou, W. Li, Y. Zhang, P. Ogunbona, D. T. Nguyen, and H. Zhang.\r\nDiscriminative key pose extraction using extended lc-ksvd for action\r\nrecognition. In 2014 International Conference on Digital Image\r\nComputing: Techniques and Applications (DICTA), pages 1\u20138, Nov\r\n2014.","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 141, 2018"}