Abstract: In recent years,skeleton-based motion recognition tasks have received extensive attention from researchers and have made great progress in research.As powerful and effective model paradigms,graph convolutional networks and convolutional neural networks are also favored by researchers in the field of skeleton action recognition.However,1)most GCN-based methods use the paradigm of modeling spatial features and temporal features alternately,which hinders the direct communication of spatial-temporal information;2)For CNN-based methods,they effectively model spatial-temporal information.However,compared with GCN-based methods,they do not make good use of spatial information.In order to solve the above problems,this paper proposes a novel method called Spatial-Temporal Joint Mapping(STJM).The proposed method not only combines the topological information of the graph in GCN-based methods,but also uses CNN-based methods to aggregate spatial-temporal information simulta-neously.Compared with the traditional GCN method, the STJM maps the nodes in high dimension and has stronger ideographic ability.After high-dimensional mapping of nodes,only a simple τ×K convolution kernel is needed to aggregate both temporal and spatial features.As a novel spatial-temporal information aggregation module,many GCN-based topology enhancement strategies can be applied to STJM block.Compared with the previous spatial-temporal simultaneous aggregation model,the proposed me-thod has better performance.Experiments show that combining the proposed STJM Block as a plug-and-play module with GCN exceeds the previous state-of-the-art models on two large-scale datasets:NTU RGB+D 60 and NTU RGB+D 120.

Key words: GCN,CNN,Action recognition,Spatial-Temporal modeling,Skeleton sequence