Graph convolutional networks skeleton-based action recognition for continuous data stream: a sliding window approach - Delamare, M.; Laville, C.; Cabani, A.; Chafouk, H. (Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021), 2021)
This paper introduces a novel deep learning-based approach to human action recognition. The method consists of a Spatio-Temporal Graph Convolutional Network operating in real-time thanks to a sliding window approach. The proposed architecture consists of a fixed window for training, validation, and test process with a Spatio-Temporal-Graph Convolutional Network for skeleton-based action recognition. We evaluate our architecture on two available datasets of common continuous stream action recognition, the Online Action Detection dataset, and UOW Online Action 3D datasets. This method is utilized for temporal detection and classification of the performed action recognition in real-time.