TY  - EJOU
AU  - Gao, Yuxin 
AU  - Duan, Xiaodong 
AU  - Dai, Qiguo 

TI  - Skeleton-Based Action Recognition Using Graph Convolutional Network with Pose Correction and Channel Topology Refinement
T2  - Computers, Materials \& Continua

PY  - 2025
VL  - 83
IS  - 1
SN  - 1546-2226

AB  - Graph convolutional network (GCN) as an essential tool in human action recognition tasks have achieved excellent performance in previous studies. However, most current skeleton-based action recognition using GCN methods use a shared topology, which cannot flexibly adapt to the diverse correlations between joints under different motion features. The video-shooting angle or the occlusion of the body parts may bring about errors when extracting the human pose coordinates with estimation algorithms. In this work, we propose a novel graph convolutional learning framework, called PCCTR-GCN, which integrates pose correction and channel topology refinement for skeleton-based human action recognition. Firstly, a pose correction module (PCM) is introduced, which corrects the pose coordinates of the input network to reduce the error in pose feature extraction. Secondly, channel topology refinement graph convolution (CTR-GC) is employed, which can dynamically learn the topology features and aggregate joint features in different channel dimensions so as to enhance the performance of graph convolution networks in feature extraction. Finally, considering that the joint stream and bone stream of skeleton data and their dynamic information are also important for distinguishing different actions, we employ a multi-stream data fusion approach to improve the network’ s recognition performance. We evaluate the model using top-1 and top-5 classification accuracy. On the benchmark datasets iMiGUE and Kinetics, the top-1 classification accuracy reaches 55.08% and 36.5%, respectively, while the top-5 classification accuracy reaches 89.98% and 59.2%, respectively. On the NTU RGB+D dataset, for the two benchmark settings (X-Sub and X-View), the classification accuracy achieves 89.7% and 95.4%, respectively.
KW  - Pose correction; multi-stream fusion; GCN; action recognition

DO  - 10.32604/cmc.2025.060137