## 3 Video Application Based on Deep Learning Algorithm
### Discriminant Deep Architecture
#### 3.2.1 CNN  
The model output of a CNN [29; 30] has no feedback connection with itself, so it is a kind of feed-forward deep network. It consists of an input layer, a convolution layer, a pooling layer, a full connection layer and an output layer. The input layer is used mainly to preprocess the original data, to reduce the impact of differences. As the most important layer of a CNN, the convolutional layer is composed of multiple feature graphs, each of which is composed of multiple neural nodes. The convolution kernel is a weight matrix: the convolution effect of convolution kernels of different sizes will be different when the extraction target is the same. The convolutional layer extracts the specific features of all nodes on the feature graph through the sliding convolution kernel, and the sliding step is expressed as the length of a sliding of the convolution kernel. Each neural node in the convolution layer is locally connected with the feature graph of the corresponding input layer through a set of weights [26]; then, its local weighting sum is mapped by a nonlinear function to obtain the data output of the convolution layer. Importantly, its weights are shared when extracting feature data on the same feature graph. The pooling layer is the output layer of the convolution layer, which is also composed of multiple feature graphs, and each feature graph corresponds to the convolution layer one by one. Average pooling and maximum pooling are common methods in this condition which can reduce the resolution of the feature surface while ensuring that the feature data will not be affected by spatial changes [31], reduce the dimension of the feature data by removing redundant information and prevent over-fitting to a certain extent. The purpose of pooling is to reduce the computational complexity. The connection layer's purpose is to integrate local information with category distinctions, after alternating multiple convolutional and pooling layers [32]. It becomes very difficult to deal with the problem of image distortion when encountering bad weather such as heavy fog and rainy nights. Image quality assessment [33] is mainly used to predict the perceptual quality of digital images, and CNN models are found to be robust to distortion [34]. [PERSON], [PERSON] [35] proposed a depth-based no-reference image-quality assessment architecture that incorporates multiple CNN models, which effectively evaluates image quality by considering multiple image quality scores from different CNN models. This architecture was confirmed in experimental tests. [PERSON] et al. [36] described a new method, based on deep learning, which removes multiple error sources in sensor signals such as cameras at the same time in the laboratory environment. By correctly identifying the classified signal, the CNN algorithm is developed, which essentially eliminates the sensor error source. In order to test the efficiency of the algorithm, the results are compared with the traditional six-bit static test, the rate test and other methods. An accuracy of 80% is achieved in correctly identifying accelerometer and gyroscope signals. [PERSON] et al. [37] introduced a case study of a CNN-based solution using camera video data for real-time open-air off-street intelligent parking management. Experiments were carried out on real-time 24 h data from the input camera video source installed in the parking lot of IIT Hyderabad (IITH). The experimental results show that this scheme can improve parking performance. [PERSON] et al. [38] proposed a scene representation method, that is, a multi-channel occupation grid graph (OGM) to describe the whole traffic scene. The deep learning architecture of the OGM is used to predict the future traffic scene. By using this 2D traffic scene representation, the future prediction can be modeled as a video processing problem in which the future time series image series needs to be predicted.