# Human Action Recognition Based on Improved Two-Stream Convolution Network
###### Abstract
[30] proposed a gated recurrent unit (GRU). [PERSON] et al. [31] proposed Bidirectional Gate Recurrent Unit (BiGRU) based on GRU for supervised and language-independent context-sensitive lemmatization. This composite deep neural network structure exhibits an outstanding ability to capture contextual information for a given word. These neural networks with the ability of memory can well complete the tasks of emotion analysis and named entity disambiguation in the field of NLP combined with context. Similar to lemmatization being context-sensitive, an action is also sensitive to its state before and after it, which is a higher-dimensional \"context\". We intend to introduce BiGRU into the field of action recognition to improve the neural network's ability to perceive pre- and post-action states (the coherence feature of action appearance). In terms of attention mechanism, [PERSON], [PERSON] et al. [32] proposed the attention mechanism SimAM based on neuroscience theory. Compared with the two-dimensional attention weights provided by the existing channel attention and spatial attention modules, SimAm provides 3D attention weights for the feature maps in the network, which is consistent with the feature dimension of action recognition tasks. At the same time, SimAm can effectively improve the accuracy of the neural network. Another advantage of SimAm attention is that most of the operators are chosen according to the solution of the defined energy function, thus avoiding excessive structural tuning work [32]. However, many action recognition algorithms still face some problems:1. The main task of a spatial stream network in the classical Two-Stream network model is to extract the appearance features of actions, and its feature extraction method is only single frame recognition (still frame). However, the appearance features of actions may have great differences in different stages. The way of single frame recognition will make the neural network unable to learn these coherence features of appearance. 2. At present, there is still room for improvement in the accuracy and stability of the classical two-stream network for action recognition. In addition, the classical Two-Stream network treats each pixel equally, which will lead the network to extract features weakly related to the action recognition task, such as video background. Therefore, a neural network needs a method to filter irrelevant information. In this paper, we construct a new network structure (BS-2 SCN) based on a two-stream convolution network to solve the two problems mentioned above. To solve these two problems, we add a bidirectional gated unit to the spatial stream network model of a two-stream convolution network to form a BiGRU network. For a video clip, we no longer only recognize a single frame, but use the method of uniform sampling to input multiple frames of images into the BiGRU network in order, and use its memory ability to improve the perception ability of a neural network to the coherence characteristics of action appearance. Due to the BiGRU network, the neural network can make use of the past and future information at the same time. In addition, we also inserted the SimAM attention mechanism into the ResNet unit, which further improves the accuracy of action recognition. To sum up, this paper has made the following contributions:  
1. Propose a new network structure based on the two-stream convolution network model and combined with a bidirectional gated recurrent unit. This network structure can well solve the shortcomings of the original neural network model in the perception of motion appearance coherence features. 2. Combine the SimAM attention mechanism, which is based on the spatial inhibition effect of neurons, with the ResNet network organically to improve the accuracy and stability of action recognition.