An enhanced spatial-temporal graph convolution network with high order features for skeleton-based action recognition.

ABSTRACT: Skeleton-based action recognition has emerged as a promising field within computer vision, offering structured representations of human motion. While existing Graph Convolutional Network (GCN)-based approaches primarily rely on raw 3D joint coordinates, these representations fail to capture higher-order spatial and temporal dependencies critical for distinguishing fine-grained actions. In this study, we introduce novel geometric features for joints, bones, and motion streams, including multi-level spatial normalization, higher-order temporal derivatives, and bone-structure encoding through lengths, angles, and anatomical distances. These enriched features explicitly model kinematic and structural relationships, enabling the capture of subtle motion dynamics and discriminative patterns. Bui

SUBMITTER: Al-Hakimi MH 

PROVIDER: S-EPMC12510661 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature