2023/07/18 by Dustin Aganian, Aganian, Dustin, Mona Köhler +7 · 1 citation
Computer Science · Engineering · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Hand Gesture Recognition Systems #Human Pose and Action Recognition #Machine Learning (cs.LG) #Robot Manipulation and Learning #Robotics (cs.RO)
paper · pdf · doi:10.48550/arxiv.2307.09238
openalex publication_date 2023/07/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
As collaborative robots (cobots) continue to gain popularity in industrial manufacturing, effective human-robot collaboration becomes crucial. Cobots should be able to recognize human actions to assist with assembly tasks and act autonomously. To achieve this, skeleton-based approaches are often used due to their ability to generalize across various people and environments. Although body skeleton approaches are widely used for action recognition, they may not be accurate enough for assembly actions where the worker's fingers and hands play a significant role. To address this limitation, we propose a method in which less detailed body skeletons are combined with highly detailed hand skeletons. We investigate CNNs and transformers, the latter of which are particularly adept at extracting and combining important information from both skeleton types using attention. This paper demonstrates the effectiveness of our proposed approach in enhancing action recognition in assembly scenarios.