Abstract: Action recognition methods have achieved significant success in the field of computer vision.Graph convolutional networks(GCNs) are crucial techniques for action recognition tasks,especially for extracting features from graph-structured data.However,existing GCNs suffer from limitations such as an excessive reliance on predefined skeleton topological graphs and a lack of flexibility in handling large temporal convolution kernels,which significantly constrain their expressive power and robustness.In this paper,we propose an adaptive bottleneck multi-scale graph convolutional action recognition method based on skeleton data.The adaptive spatial module optimizes the skeleton topological graph structure and parameters,enhancing the model's flexibi-lity.The bottleneck layer multi-scale temporal module improves the temporal modeling capabilities while reducing channel width to save computational costs and parameters.Experimental results on large-scale skeleton action recognition datasets,NTU-RGB+Dand NTU-RGB+D 120,show that the accuracy of our model is improved to a certain extent.

Key words: Action recognition, Skeleton modality, Graph convolution network, Video classification, Computer vision