HIGHLIGHTS
- What: To address these challenges in action analysis the authors propose an intelligent action recognition method based on both skeleton and video features aiming to replace manual decomposition of action elements. In the manufacturing context, where extensive datasets on work procedure actions are scarce, this study focuses on the noteworthy performance enhancement achieved by integrating improved attention mechanisms into the model. The authors focus on intelligent recognition using skeleton and video features, creating independent datasets for each format.
- Who: Attention and colleagues from the School of Mechanical Engineering, DongGuan University of Technology, Donguan, China have . . .
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