# Human Action Recognition Based on 3D Convolution and Multi-Attention Transformer
## 1 Introduction
Although graph convolutional networks (GCNs) perform well in modeling human skeleton sequences and joint relationships, they face challenges in fusing spatiotemporal features and handling long-term temporal data. In summary, to fully leverage the advantages of various networks, we propose a two-stream network model for human action recognition based on a multi-scale attention Transformer and 3D convolution (MAT-C3D). By constructing a temporal network based on 3D convolution to capture the temporal dependencies between video frames, it can better understand the continuous changes of actions. By constructing a spatial network based on a multi-scale Transformer model, it can capture long-term dependencies and generate receptive fields of different sizes for feature fusion. Moreover, an adaptive weighting mechanism is employed for feature fusion, which enriches the diversity of features and better adapts to long-term, multi-scale action recognition tasks in complex environments. The main work and contributions are summarized as follows:  
1. A brand-new two-stream network structure containing a C3D temporal network and a multi-scale Transformer spatial network has been constructed, which fully utilizes the correlation between spatial and temporal information to extract spatiotemporal features with interactive relationships, effectively improving the action recognition ability with long-term dependencies. 2. A multi-scale convolution module is introduced to replace the original FFN module in the Transformer; after inputting context-based relative position encoding, adaptive integrated multi-scale features with stronger representation capabilities are obtained, effectively improving the recognition accuracy of the model in complex environments. 3. A more efficient feature fusion strategy has been designed, and the spatiotemporal feature fusion method of the two-stream network has been improved by using weighted fusion, which enhances the model's ability to model complex spatiotemporal interaction relationships and further improves the recognition accuracy in complex environments. 4. Experimental results on the datasets of UCF101 and HMDB51 demonstrate that our algorithm is a highly accurate human action recognition model. The rest of this paper is organized as follows: Section 2 reviews related work in action recognition, including 3D convolution, two-stream networks, and Transformer models, and discusses the limitations of existing methods. Section 3 introduces our proposed two-stream network of action recognition based on Transformer and C3D, including a C3D time-stream convolutional network, multi-scale convolution Transformer, positional encoding, and a multi-scale residual model. Section 4 discusses the feature fusion methods and identified the optimal fusion strategy. Section 5 describes the experimental setup, including the software and hardware environments and the used datasets, and presents the results of our experiments, including the main results, ablation studies, and an extended analysis on experimental results. Finally, Section 6 concludes the paper.