Human Skeleton Action Recognition Algorithm Based on Dynamic Topological Graph

Abstract

Traditional human skeleton action recognition algorithms manually construct topological graphs to model the action sequence contained in multiple video frames and learn each video frame to reflect the data changes,which may lead to the high computational cost,low network generalization performance and catastrophic forgetting.To solve these problems,a human skeleton action recognition algorithm based on dynamic topological graph is proposed,in which the human skeleton topological graph is dynamically constructed based on continuous learning.Specifically,human skeleton sequence data with multi-relationship characte-ristics are recoded into relationship triplets,and feature embedding is learned in a decoupling manner via the long short-term me-mory network.When handling new skeleton relationship triplets,we dynamically construct the human skeleton topological graph by a partial update mechanism,and then send it to the skeleton action recognition algorithm based on spatio-temporal graph convolution network for action recognition.Experimental results demonstrate that the proposed algorithm achieves 40%,85% and 90% recognition accuracy on three benchmark datasets,namely Kinetics-Skeleton,NTU-RGB+D(X-Sub) and NTU-RGB+D(X-View),respectively,which improve the accuracy of human skeleton action recognition.

Keywords

• human action recognition|human skeleton data|catastrophic forgetting|continual learning|graph convolution network