## 3 Video Application Based on Deep Learning Algorithm
### Discriminant Deep Architecture
#### 3.2.2 Rnn  
There is a correlation between the data when the data has sequence characteristics and the accurate feature representation cannot be obtained by analyzing and extracting features from a single data. In order to solve this problem, the RNN was proposed by [PERSON] et al. [56]. When an RNN is faced with time series data, according to its special network structure, the information of the previous time can be mapped non-linearly to the next time, and the dependency relationship in the data is maintained, which gives RNN the ability to remember and process the previous data information so that it can extract features from the sequence data. For RNN, its weight parameters are shared during a training process.  
After follow-up practice, it is found that it is difficult for RNN to deal with long-term and long-distance dependence problems, which often lead to RNN gradient disappearance or gradient explosion [57]. The long-term short-term memory (LSTM) model proposed by [PERSON] et al. [58] can deal with the above problems well. The structure of LSTM is relatively complex, and its memory unit includes three kinds of gating units: the forgetting gate, the input gate and the output gate. The forgetting gate can filter the historical data and discard the information that feels useless in the historical information, the input gate can control the storage of part of the current data into the memory unit and part of the stored information in the output control memory unit is output at the current time. Because of the above features, LSTM can better store useful information and plays an important role in solving the problems of long-term and long-distance dependence. As a further study of LSTM, gated cyclic unit neural networks (GRUs) couple the input gate and the forgetting gate into a gated unit, which means the reduction of parameters and the reduction of computational complexity. Therefore, compared with LSTM, GRU has better training efficiency.  
[PERSON] et al. [59] designed a sequence-to-sequence deep learning model called DeepChannel that is based on a codec. It can predict the change of wireless signal strength in the future according to the past signal strength data. This article considers two different versions of DeepChannel; the first and second versions use LSTM and GRU as their basic unit structures, respectively. Different from the previous work of designing models for specific network settings, DeepChannel has strong adaptability and can predict future channel conditions for different networks, sampling rates, mobility modes and communication standards.  
[PERSON] et al. [60] proposed a unified driver behavior modeling system for multi-scale behavior recognition. The driver behavior recognition system aims to identify the physical and mental state of the driver simultaneously based on the deep encoder-decoder framework. The model learns to recognize three different time scales of driver behavior: mirror examination and facial expression state, as well as two psychological behaviors, including intention and emotion. The encoder module is based on CNN and is used to capture spatial information from the input video stream. Then, several decoders for different driver state estimations are proposed using RNN based on full connection (FC) and LSTM. The model framework is shown in Figure 5.