Work procedure action recognition based on skeleton and video features

Abstract

In the field of industrial engineering, traditional methods for analyzing manufacturing process actions have limitations such as time-consuming, labor-intensive, and experience-dependent. To address these challenges in action analysis, we propose an intelligent action recognition method based on both skeleton and video features, aiming to replace manual decomposition of action elements. The MediaPipe framework is used for human posture estimation to obtain the skeleton sequence, and the CNN-GRU model is constructed action recognition based on skeleton features. For hand movements involving the use of industrial gloves, an enhanced TimeSformer video understanding model is introduced for action recognition based on video features. This improvement incorporates uniform attention and external attention mechanisms, resulting in enhanced model performance. The final experimental validation for the self-constructed process action dataset shows that the online detection speed of the skeleton action recognition model reaches 25 FPS, and the accuracy of the end-to-end video action recognition model is improved by 10.5 percentage points compared to the base model.