This research presents a novel approach to skeleton-based action segmentation by leveraging text-derived relational graphs to improve the recognition of human movements.

• Captures intrinsic correlations between joints and actions that traditional methods miss
• Utilizes relational graphs to model dependencies among skeletal joints more effectively
• Enables more accurate detection of complex human actions in untrimmed video sequences
• Offers significant improvements for security applications in behavior monitoring and threat identification

For security professionals, this technology provides enhanced capabilities for surveillance systems, anomaly detection, and potential threat identification through more precise human movement analysis.

Text-Derived Relational Graph-Enhanced Network for Skeleton-Based Action Segmentation