# Human Action Recognition Based on Improved Two-Stream Convolution Network
###### Abstract  
Two-stream convolution network (2 SCN) is a classical method of action recognition. It is capable of extracting action information from two dimensions: spatial and temporal streams. However, the method of extracting motion features from a spatial stream is single-frame recognition, and there is still room for improvement in the perception ability of appearance coherence features. The classical two-stream convolution network structure is modified in this paper by utilizing the strong mining capabilities of the bidirectional gated recurrent unit (BiGRU) to allow the neural network to extract the appearance coherence features of actions. In addition, this paper introduces an attention mechanism (SimAM) based on neuroscience theory, which improves the accuracy and stability of neural networks. Experiments show that the method proposed in this paper (BS-2 SCN, BiGRU-SimAM Two-stream convolution network) has high accuracy. The accuracy is improved by 2.6% on the UCF101 data set and 11.7% on the HMDB51 data set. action recognition; two-stream convolution network; BiGRU network; SimAM attention mechanism +
Footnote †: journal: Medical  
Yang, [PERSON] et al. [9] proposed a skeleton action recognition method based on 3D depth data, which achieved good results in online action recognition scenes. However, the manual feature extraction method is sensitive to the direction, position, and background environment of human action, and needs an accurate action template as support. It has high accuracy only for simple actions in limited scenes. Deep learning can automatically extract features when the model is uncertain, and has been widely used in object detection [10], automatic control [11], remote sensing image processing [12], system parameter identification [13] and other fields. In recent years, with the continuous improvement of computer computing power, deep learning has been deeply developed, and human action recognition algorithms based on deep learning have gradually emerged. At present, the more commonly used deep learning network is the classical convolutional neural network (CNN). CNNs are widely used in many image semantic segmentation tasks [14]. The derivative networks based on CNN include AlexNet [15], VGG network [16], GoogleNet [17], ResNet [18] etc. These networks can extract features from a single image and classify them and have achieved good results on ImageNet. The action recognition algorithms based on these neural networks are mainly divided into three categories: (1) Single-stream network model. In this network model, the three-dimensional convolutional neural network proposed by [PERSON], [PERSON] et al. [19], namely 3D CNN is widely used. Its convolution network structure includes a hard connection layer, three 3D convolution layers, two pooling layers, and a full connection layer. (2) Two-Steam network model. Inspired by the two-stream hypothesis of neuroscience, [PERSON] et al. [20] creatively proposed the two-stream network model for the first time in 2014. (3) Multi-Stream network model. This network model is an extension of the Two-Steam network model. Based on the Two-Steam network model, other deep learning networks are added to extract different features. Among the three kinds of action recognition algorithms, the Two-Steam network model method has good generalization and expansibility. Therefore, people have proposed many improved models based on this model, such as adding the attention mechanism module to the Two-Steam network model to form the structure of the \"Two-Steam network model + attention mechanism\". The existing attention mechanisms include circular attention mechanism [21], global attention mechanism [22], SE attention mechanism [23], CBAM attention mechanism [24], DA attention mechanism [25], SAM attention mechanism [26], multi-head attention mechanism [27], etc. In early studies, people proposed classical recurrent neural network (RNN) [28] and long short-term memory network (LSTM) [29]. Afterwards, [PERSON], [PERSON] et al.