Wuyan Liang1, Xiaolong Xu2,*
CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1089-1103, 2024, DOI:10.32604/cmc.2024.047861
Abstract Recognition of human gesture actions is a challenging issue due to the complex patterns in both visual and skeletal features. Existing gesture action recognition (GAR) methods typically analyze visual and skeletal data, failing to meet the demands of various scenarios. Furthermore, multi-modal approaches lack the versatility to efficiently process both uniform and disparate input patterns. Thus, in this paper, an attention-enhanced pseudo-3D residual model is proposed to address the GAR problem, called HgaNets. This model comprises two independent components designed for modeling visual RGB (red, green and blue) images and 3D skeletal heatmaps, respectively. More specifically, each component consists of… More >
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