## 3 Video Application Based on Deep Learning Algorithm  
CVIS has the characteristics of a dynamic topology and a time-varying channel state. The traditional video application methods can not accurately analyze and extract the deep features of traffic data. The computational complexity of the traditional method is significantly improved, resulting in dimensional disaster [24] when the dimension of the dataset is high. By taking into account relatively few variables, the traditional methods can not reflect the real traffic characteristics of the CVIS environment. In addition, the traditional method's model is relatively simple, and it is easy to find the local optimal solution. However, deep learning can deal with these problems very well.  
Deep learning is an important branch of machine learning, which originates from the study of artificial neural networks and is a further deepening of the basis of traditional neural networks. Shallow networks, such as most classification and regression algorithms, are limited to showing good performance in dealing with complex high-dimensional problems when the sample size is not large enough and the computing power is weak. On the other hand, deep learning mainly completes the complex function approximation through a deep nonlinear network structure, and can extract an abstract distributed representation of its essential features from a small number of samples [25; 26]. [PERSON] et al. [2] proposed a fast learning algorithm based on a deep belief network (DBN) which is mainly divided into two steps: firstly, unsupervised learning is used to pre-train the constrained Boltzmann machine (RBM) network at each layer, and then supervised learning is used to fine-tune the whole DBN. This model reduces the difficulty of deep structure optimization, so the development and application of deep learning are paid increased attention by researchers. Because of the differences in the structure and training methods, deep learning can be divided into the following three categories:  
1. Generative deep architecture: this architecture can be described as a model for generating data which belongs to a probability model. Through the joint probability distribution of the observed data and the corresponding categories, the feature set of the generated data contains the high-order correlation features of the input dataset, which is more like an unsupervised learning method;
2. Discriminant deep architecture: this architecture classifies patterns through its own discriminant ability, which estimates a posteriori probability through the conditional probability distribution of the observed data, similar to a kind of supervised learning;
3. Mixed deep architecture: this architecture combines the advantages of generative and discriminative deep architecture, and has excellent expression ability and discriminant ability.  
Based on the above three architectures, the advantages of algorithms in the field of video application over CVIS were summarized, respectively. At present, most of the research methods in the field are based on the generative and discriminant models, and the research on the hybrid model is still lacking. In addition, deep reinforcement learning also promotes the further development of deep learning, so this paper also summarized the research in this field.