Abstract
A keyframe extraction and process recognition method for assembly operation is proposed based on density clustering to solve the problems of data redundancy and difficulty in obtaining valid data frames from the process of continuous assembly operation. A standard operation gesture set including dynamic and static action was constructed through assembly operation decomposing. Finger feature variables and gesture comprehensive feature quantized function were defined according to finger joint structure. Based on searching for local extreme points of the function, density clustering method was used to extract the keyframes of the assembly operation sequence to eliminate the redundant data. Finally, the support vector machine algorithm model and the Levinstein distance were determined to complete the keyframe recognition and assembly operation matching. A case study showed that the method presented can effectively discretize the assembly operation sequence, remove approximately 84% of redundant data frames, and achieve a comprehensive recognition rate of 92%.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
