# I3D-Shufflenet Based Human Action Recognition
## 1 Introduction  
With the development of artificial intelligence, the progress of computer vision has received special attention. At present, the world's top scientific research teams and major scientific research institutions are achieving rapid progress in the field of human action recognition. In the 1970s, a human body description model was proposed by Professor [PERSON] [1], which had a great impact on human body recognition. Video-based human action recognition methods traditionally make use of artificial means to extract motion features. The traditional algorithms for human action recognition consist of Histogram of Oriented Gradients (HOG) [2], Histogram of Optical Flow (HOF) [3], Dense Trajectory (DT) [4] etc. The DT algorithm performs multi-scale division of each frame of the video. After division, the features of each region are obtained by dense sampling based on the grid division method. The time domain feature is extracted to generate the feature of trajectory and then the next trajectory position is predicted when the features of the entire picture are obtained. In recent years, deep learning has developed rapidly. It was widely used in the field of image recognition. With the development of deep learning, it is widely used in the field of human action recognition, which greatly improves the accuracy of human action recognition. Deep learning is a data processing and feature learning method. Through lower-level features extraction of the image, the high-level image features or image attributes can be shaped according to lower-level features, then the human action and movement features can be extracted. At present, the performance of deep learning algorithms for big data is significantly superior to traditional algorithms, and has achieved good performance in computer vision and speechrecognition. Convolutional Neural Network (CNN) was proposed in 2012 because it is appropriate for image processing. However, the temporal features extraction is limited due to the loss of time domain information during the feature extraction for video. The time domain features cannot be effectively extracted by 2D-CNN. Under the background of the great success of convolutional networks in the field of computer vision, graph neural networks (GCNs) based human action recognition become a recent event [5]. [PERSON] et al. (2013) proposed the important research on graph convolutional networks, and the space-based graph convolutional networks develops rapidly. These methods directly perform convolution on the graph structure through gathering information about neighboring nodes. In recent years, 3D neural networks have been fully developed. The concept of 3D neural networks was first proposed by [PERSON] et al. at 2005 [6] and further clarified by [PERSON] et al. at 2009 [7]. The early studies propagating the adjacent information iteratively through the recursive neural network until reaching a stable point, then the representation of the target node is obtained. This process requires massive calculation. Many recent studies are devoted to solving this problem. [PERSON] et al. [8] proposed a three-dimensional convolutional neural network for spatial-temporal features extraction. The 2D-CNN can be extended to 3D-CNN. 3D convolutional neural network (Convolution Neural Network, CNN) has a good effect in comprehensively image features extraction. The original two-dimensional network still extracts the spatial features of static images, and the time domain features is extracted by the third dimension, it can also convolve across multiple frames of images and extract the information before and after the time series. However, with the augment of parameter, the training of the model becomes difficult. A residual network structure was proposed by [PERSON] et al. [9], which can not only handle the above-mentioned problems caused by increasing the network depth, but also optimize and improve the network performance. Two-stream [10] is dedicated to RGB images and optical flow graphs, then the results of the two are subjected to a fusion.