TY - JFULL AU - Insaf Ajili and Malik Mallem and Jean-Yves Didier PY - 2018/10/ TI - Relevant LMA Features for Human Motion Recognition T2 - International Journal of Computer and Information Engineering SP - 791 EP - 796 VL - 12 SN - 1307-6892 UR - https://publications.waset.org/pdf/10009574 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 141, 2018 N2 - 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. ER -