Verification of View-Invariant and Fine-Grained Action Recognition Using 3D Skeleton

We are developing a fine-grained action recognition system to detect human errors with the aim of achieving Quality Assurance. Recently, there have been numerous studies utilizing multiple sensors and viewpoints to enhance the accuracy of action recognition. However, in manufacturing sites, available locations for camera included sensor installation are often limited, making it difficult to attach cameras to fixed positions. In this study, we propose a method for view-invariant action recognition using 3D skeleton data. By converting the 3D skeleton data into a body-coordinate system and utilizing it for training, we have developed and evaluated a model capable of recognizing actions from unknown viewpoints. Furthermore, challenges that need to be addressed before implementing the proposed method in practical manufacturing environments are discussed.