In this paper, we present a new approach for human action recognition using 3D skeleton joints recovered from RGB-D cameras. We propose a descriptor based on differences of skeleton joints. This descriptor combines two characteristics including static posture and overall dynamics that encode spatial and temporal aspects. Then, we apply the mean function on these characteristics in order to form the feature vector, used as an input to Random Forest classifier for action classification. The experimental results on both datasets: MSR Action 3D dataset and MSR Daily Activity 3D dataset demonstrate that our approach is efficient and gives promising results compared to state-of-the-art approaches.
['Abdelouahid Ben Tamou', 'Lahoucine Ballihi', 'Driss Aboutajdine']
Automatic Learning of Articulated Skeletons Based on Mean of 3D Joints for Efficient Action Recognition