Abstract
Traditional convolution neural networks have achieved great success in human action recognition. However, it is challenging to establish effective associations between different human bone nodes to capture detailed information. In this paper, we propose a dual attention-guided multiscale dynamic aggregate graph convolution neural network (DAG-GCN) for skeleton-based human action recognition. Our goal is to explore the best correlation and determine high-level semantic features. First, a multiscale dynamic aggregate GCN module is used to capture important semantic information and to establish dependence relationships for different bone nodes. Second, the higher level semantic feature is further refined, and the semantic relevance is emphasized through a dual attention guidance module. In addition, we exploit the relationship of joints hierarchically and the spatial temporal correlations through two modules. Experiments with the DAG-GCN method result in good performance on the NTU-60-RGB+D and NTU-120-RGB+D datasets. The accuracy is 95.76% and 90.01%, respectively, for the cross (X)-View and X-Subon the NTU60dataset.
Subject
Physics and Astronomy (miscellaneous),General Mathematics,Chemistry (miscellaneous),Computer Science (miscellaneous)
Reference43 articles.
1. A Video Representation Method Based on Multi-View Structure Preserving Embedding for Action Retrieval
2. An end-to-end deep learning model for human activity recognition from highly sparse body sensor data in Internet of Medical Things environment;Hassan;J. Supercomput.,2008
3. Open Pose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields;Cao;IEEE Trans. Pattern Anal. Mach. Intell.,2018
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