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Relevant LMA Features for Human Motion Recognition

Authors: Insaf Ajili, Malik Mallem, Jean-Yves Didier

Abstract:

Motion recognition from videos is actually a very complex task due to the high variability of motions. This paper describes the challenges of human motion recognition, especially motion representation step with relevant features. Our descriptor vector is inspired from Laban Movement Analysis method. We propose discriminative features using the Random Forest algorithm in order to remove redundant features and make learning algorithms operate faster and more effectively. We validate our method on MSRC-12 and UTKinect datasets.

Keywords: Human motion recognition, Discriminative LMA features, random forest, features reduction.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1474709

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References:

1] I. Ajili, M. Mallem, and J. Y. Didier. Gesture recognition for humanoid robot teleoperation. In 2017 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pages 1115–1120, Aug 2017.
[2] I. Ajili, M. Mallem, and J.-Y. Didier. Robust human action recognition system using laban movement analysis. Procedia Computer Science, 112(Supplement C):554 – 563, 2017. Knowledge-Based and Intelligent Information & Engineering Systems: Proceedings of the 21st International Conference, KES-20176-8 September 2017, Marseille, France.
[3] C. B. Barber, D. P. Dobkin, and H. Huhdanpaa. The quickhull algorithm for convex hulls. ACM Trans. Math. Softw., 22(4):469–483, Dec. 1996.
[4] L. Breiman. Random forests. Mach. Learn., 45(1):5–32, Oct. 2001.
[5] A. B.Surendiran1. Feature selection using stepwise anova discriminant analysis for mammogram mass classification. International Journal on Signal & Image Processing, 2(1):4, January 2011.
[6] S. Fothergill, H. Mentis, P. Kohli, and S. Nowozin. Instructing people for training gestural interactive systems. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI ’12, pages 1737–1746, New York, NY, USA, 2012. ACM.
[7] M. E. Hussein, M. Torki, M. A. Gowayyed, and M. El-Saban. Human action recognition using a temporal hierarchy of covariance descriptors on 3d joint locations. In Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, IJCAI ’13, pages 2466–2472. AAAI Press, 2013.
[8] I. Laptev and T. Lindeberg. Space-time interest points. In Computer Vision, 2003. Proceedings. Ninth IEEE International Conference on, pages 432–439. IEEE, 2003.
[9] C. Lazar, J. Taminau, S. Meganck, D. Steenhoff, A. Coletta, C. Molter, V. de Schaetzen, R. Duque, H. Bersini, and A. Nowe. A survey on filter techniques for feature selection in gene expression microarray analysis. IEEE/ACM Trans. Comput. Biol. Bioinformatics, 9(4):1106–1119, July 2012.
[10] A. M. Lehrmann, P. V. Gehler, and S. Nowozin. Efficient nonlinear markov models for human motion. In 2014 IEEE Conference on Computer Vision and Pattern Recognition, pages 1314–1321, June 2014.
[11] H. Wang, A. Kl¨aser, C. Schmid, and C.-L. Liu. Dense trajectories and motion boundary descriptors for action recognition. Int. J. Comput. Vis., 103(1):60–79, 2013.
[12] P. Wang, Z. Li, Y. Hou, and W. Li. Action recognition based on joint trajectory maps using convolutional neural networks. CoRR, abs/1611.02447, 2016.
[13] J. Weston, S. Mukherjee, O. Chapelle, M. Pontil, T. Poggio, and V. Vapnik. Feature selection for svms. In Proceedings of the 13th International Conference on Neural Information Processing Systems, NIPS’00, pages 647–653, Cambridge, MA, USA, 2000. MIT Press.
[14] L. Xia, C. C. Chen, and J. K. Aggarwal. View invariant human action recognition using histograms of 3d joints. In 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pages 20–27, June 2012.
[15] M. Yamada, W. Jitkrittum, L. Sigal, E. P. Xing, and M. Sugiyama. High-dimensional feature selection by feature-wise non-linear lasso. ArXiv e-prints, Feb. 2012.
[16] L. Zhou, W. Li, Y. Zhang, P. Ogunbona, D. T. Nguyen, and H. Zhang. Discriminative key pose extraction using extended lc-ksvd for action recognition. In 2014 International Conference on Digital Image Computing: Techniques and Applications (DICTA), pages 1–8, Nov 2014.