## 3 Video Application Based on Deep Learning Algorithm
### Discriminant Deep Architecture
#### 3.2.1 CNN
Different from the traditional deep regression network, which usually uses the mean square error directly as the loss function, robust metric learning is used to train the network effectively. On this basis, multiple networks are combined together to further improve the generalization ability. Extensive experiments have been carried out and it is proved that the proposed model is effective. A region-based CNN algorithm (RCNN) was proposed by [PERSON] et al. in 2014 [46]. The author applies a CNN to the field of target detection for the first time. The algorithm is mainly composed of four parts: candidate region generation, feature extraction, category judgment and location refinement. Because of its excellent performance, RCNN is widely used in the field of video applications over CVIS. [PERSON] et al. [47] proposed an RCNN algorithm to deal with the aggregation of vehicle data from real-time video streams which improves the detection accuracy by 3.5% compared with a CNN. [PERSON] et al. [48] prepared datasets containing real data (taken from military performance videos) and toy data (downloaded from YouTube videos). The datasets were divided into three main types: military vehicles, non-military vehicles and other non-vehicle objects. In order to analyze the adequacy of the prepared datasets, we use algorithms such as RCNN target detection to distinguish between military and non-military vehicles. The experimental results show that using the customized/prepared datasets to train the deep architecture can identify seven kinds of military vehicles and four kinds of non-military vehicles. You Only Look Once (YOLO) was proposed by [PERSON] et al. in 2015 [49]. YOLO is a target detection algorithm based on CNN, which means that target features can be detected only once. Because of this feature, YOLO is favored by researchers in the field of real-time video target detection. [PERSON] et al. [50] devoted themselves to developing benchmark applications for real-time traffic incident identification and related traffic management using real-time congestion-aware navigation of intelligent vehicles with video feeds, proposing an algorithm framework based on YOLO for identifying and classifying traffic events, such as traffic accidents and congestion and building a fog layer between edge nodes and clouds to make distributed computing (servers) and provide storage closer to edge nodes. Experiments show that the total latency or response time of the YOLO-based fog-cloud platform is lower than that of the pure cloud platform. [PERSON] et al. [51] proposed a target detection framework based on YOLO to detect and track a large number of highly mobile vehicles, which is also considered as the region of interest (ROI) in vehicle optical camera communication (OCC) systems. The author tested the method on a rainy night on a Korean highway to analyze the effectiveness of this method in the vehicle OCC system. [PERSON] et al. [52] proposed a real-time statistical algorithm framework for YOLO vehicle classification based on edge AI. Experiments show that the algorithm is helpful to provide timely traffic information. [PERSON] et al. [53] aimed to use real-time traffic data collected by traffic surveillance video and image recognition to explore the relationship between the spatio-temporal pattern of vehicle types and numbers in different urban functional areas and the emission of traffic-related air pollutants. Video-based YOLO detection technology is used to analyze the data, and air pollution is quantified by pollutant emission coefficient. [PERSON] et al. [54] proposed a deep learning model based on YOLOv2, and applied the model to traffic detection scenarios. In order to reduce the network burden and eliminate the deployment of the backbone of the network at the intersection, it is recommended that the traffic video data be processed at the edge of the network without transmitting big data back to the cloud.