Relevant LMA Features for Human Motion Recognition
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.
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.
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.
@article{"International Journal of Information, Control and Computer Sciences:77978", author = "Insaf Ajili and Malik Mallem and Jean-Yves Didier", title = "Relevant LMA Features for Human Motion Recognition", 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.", volume = "12", number = "9", pages = "792-5", }
