# Human Action Recognition Based on 3D Convolution and Multi-Attention Transformer
## 1 Introduction  
Human action recognition involves extracting and analyzing human movements and behavioral features from video sequences to determine the specific categories of actions being performed. This technology is essential for enabling machines to interpret human behavior and serves as a cornerstone in the field of computer vision. It has significant importance and broad application potential across various domains, including intelligent security, virtual reality, human-computer interaction, and intelligent caregiving, where it can deliver substantial economic and social benefits. Currently, deep learning-based human action recognition methods have attracted a great deal of attention and have achieved numerous research results. Depending on the different network architectures used, deep learning-based human action recognition methods can be categorized into four types: 3D convolutional(3D CNN) [1, 2, 3], two-stage network [4, 5], Transformer model [6, 7], and graph convolutional (GCN) [8, 9]. Three-dimensional convolution has the advantage of simultaneously modeling spatio-temporal information and has been widely used in action recognition tasks. For example, [PERSON] [1] emphasized that 3D CNNs can better integrate with multiple modalities such as RGB and optical flow information, thus improving the recognition results. [PERSON] [2] proposed a 3D deformable convolutional reasoning network (DCTR), which is used to model and reason about the dependency relationships among multiple entities in the spatiotemporaldimensions to enhance the recognition performance. Although 3D CNN-based methods can model spatio-temporal features simultaneously, there is still room for improvement in handling long-term dependencies. Two-stream networks extract spatiotemporal information through two separate branches, enabling richer feature representations and more precise extraction of motion information, leading to higher recognition accuracy. For example, [PERSON] [4] improved recognition performance via contrastive learning in the two-stream network. [PERSON] [5] designed a multimodal two-stream fusion network to enhance the saliency of action-related characteristics. Although two-stream networks can extract spatiotemporal features, the interaction and fusion strategies of the spatial and temporal streams, as well as the representation and learning methods of temporal information, still need improvement. Additionally, most methods using 2D CNN cannot effectively capture the temporal information between video frames. In recent years, Transformers have been introduced into the field of video understanding. The application of the Transformer model provides a new paradigm for action recognition. Its powerful long-range dependency modeling, multimodal fusion ability, and good scalability make it particularly suitable for handling long sequence data and complex temporal contextual relationships in videos. For example, [PERSON] [6] proposed a spatiotemporal two-stream multi-scale Transformer (STDM Transformer) model for action recognition. [PERSON] [7] proposed a Transformer-based multi-view spatiotemporal feature interaction fusion framework, which achieves effective action recognition through deep fusion of multi-view information. Although the performance of action recognition based on Transformer has greatly improved, Transformer models still face challenges such as large data requirements and insufficient local information capture. The method based on a GCN can leverage the topological structure of skeletal data to capture the relationships between different joint points, providing a more natural representation of spatiotemporal features. For example, [PERSON] [8] proposed a dynamic semantic-based graph convolutional network (DS-GCN) action recognition method. [PERSON] [9] introduced an action recognition method based on graph prototype learning (ProtoGCN).