## 3 Video Application Based on Deep Learning Algorithm
### Discriminant Deep Architecture
#### 3.2.1 CNN
In order to predict the future traffic scene according to the past traffic scene, a deep learning architecture usinga CNN and long-term short-term memory network is proposed. In the case of highly conflicting traffic, the future prediction accuracy can be as high as 90%, and the prediction range is 3 s by using the proposed deep learning framework. [PERSON] et al. [39] proposed a multi-view perceptual field codec convolutional neural network (MvRF-CNN) model, which contributes mainly to the use of multiple views of convolution kernels with residual feature fusions in the early, middle and later stages of the codec (EnDEC) architecture. The model is still effective in complex traffic environments (dynamic background, camera jitter, night video, bad weather, etc.). [PERSON] et al. [40] proposed a deep learning adaptive active network subdivision model for vehicle division. The proposed solution includes three stages: subtraction based on an adaptive background model, active network subnet using a CNN and optimization using an extended topology active network (ETAN) to extract data from the CNN results. Adaptive background modeling is based on the adaptive gain function, which is composed of pixels of frames in the video. The gain function can compensate for the shadow and lighting problems that affect the vehicle division. The deep learning-assisted topology active network deformable model can provide higher partition accuracy in the presence of occlusion, cluttered background and traffic density changes. [PERSON] et al. [41] proposed an intelligent collaborative visual perception system for networked vehicles. The driving video is collected from the vehicle and transmitted to the cloud for visual perception using a CNN. In order to receive and process multiple vehicle videos synchronously, several data pipeline CNN frameworks are developed; their frame structures are shown in Figure 4. Considering the bandwidth consumption and transmission delay, the resolution adaptive strategy and frame rate control strategy are designed, and IPv6 routing is used to transmit between the vehicle and the cloud. The system helps drivers to make better and safer decisions by enhancing their perception. [PERSON] et al. [42] combined video compression sensing (CVS) with a CNN, and proposed a correlation analysis model in the measurement domain called CVS-CNN. They used CNN instead of the pseudo-inverse transformation of the measurement matrix, and established the correlation between the measured values of the blocks to be estimated and the measured values of adjacent non-overlapping blocks, which can analyze the time correlation of video frames in the measurement domain. The processing speed, accuracy and robustness are obviously better than similar video frame correlation analysis methods. [PERSON] et al. [43] proposed an intrusion detection method based on feature generation and CNN for the first time to detect attacks on audio and video transmission protocol (AVTP) streams in a network based on automobile Ethernet. In order to evaluate the intrusion detection system, a physical test platform based on BroadR-Reach and captured real AVTP packets was built. Figure 4: Multi-data pipeline CNN framework. [PERSON] et al. [44] proposed a new heuristic unimodal method based on a vision system to estimate the collision priority of vehicles on the road. Priority is estimated from the point of view that it may be equipped with a vision-based driver assistance system or the self-driving vehicle itself. Consider crowdsourced videos from YouTube about vehicle collisions captured by vehicle onboard cameras: in order to detect the moving vehicle in the video, the pre-training object detection model and tracking algorithm based on CNN are used. [PERSON] et al. [45] proposed a robust hierarchical deep learning for promoting congestion detection. In this method, a deep network is designed for hierarchical semantic feature extraction.