MACHINE PERCEPTION AND ROBOTICS GROUP

Dept. of Robotics Science and Technology,
Chubu University

Action recognition from skeletons is gaining attention since skeleton data can be easily obtained from depth sensors and highly accurate pose estimation methods such as OpenPose. A method using graph convolutional networks (GCN) has been proposed for action recognition using skeletons as input. Among the action recognition methods using GCN, spatial temporal GCN (ST-GCN) achieves a higher accuracy by capturing skeletal data as spatial and temporal graphs. However, because ST-GCN defines human skeleton patterns in advance and applies convolution processing, it is not possible to capture features that take into account the joint relationships specific to each action. The purpose of this work is to recognize actions considering the connection patterns specific to action classes. The optimal connection pattern is obtained by acquiring features of each action class by introducing multitask learning and selecting edges on the basis of the value of the weight matrix indicating the importance of the edges. Experimental results show that the proposed method has a higher classification accuracy than the conventional method. Moreover, we visualize the obtained connection patterns by the proposed method and show that our method can obtain specific connection patterns for each action class.