# Research on Urban Traffic Incident Detection Based on Vehicle Cameras
## 2 Related Work  
The existing automatic detection technology collects data through toroidal coils or ultrasonic detectors and then processes the data. The existing traffic incident detection methods are mainly divided into the following categories:  
1. Detection algorithm based on statistical theory. In this kind of method, the sensor counts the number of passing vehicles per unit of time and indirectly calculates the algorithm of the traffic state [6; 7; 8]. It has the advantages of low cost, all-weather, etc. The current road traffic congestion level can be identified by simply analyzing the collected data. For example, by applying mathematical algorithms to these measurements, unexpected traffic flow characteristics up and down suspicious locations can be identified and flagged as traffic incidents. 2. Predict the state of traffic incidents through a time series model. This type of approach uses time-series analysis to detect traffic anomalies using features such as real-time traffic, predicted normal traffic, and the difference between the two. Chaos, Solitons and Fractals [9] use nonlinear time-series models to predict traffic states. [PERSON], [PERSON], and [PERSON] [10] analyzed traffic flow time series by clustering traffic time series with similar fluctuation patterns. [PERSON] and [PERSON] [11] proposed a technique that combines time series analysis and machine learning to improve the accuracy and efficiency of traffic incidents. 3. Through social network algorithms. With the rise of social networking tools, social networking species contain rich traffic condition information. Some scholars [12; 13] have extracted information entries and positioning of traffic accidents, traffic congestion, and other information shared by twitter residents, and detected traffic conditions and traffic incidents. 4. Video monitoring through the bayonet. With the development of the road intelligent transportation system, many roadside monitoring devices have been built on urban roads and highways for traffic monitoring. Some scholars [14; 15] have detected and tracked road traffic incidents through the video monitoring of roadside devices.The advantage of this method is that the roadside equipment has strong computing power and fast detection speed. The dis advantage is that the roadside equipment may be blocked by tree branches, and the detection accuracy is easily affected in bad weather conditions. In addition, limited by roadside equipment, it is impossible to realize accurate perception and intelligent control of traffic operation situations, and it cannot provide large-scale, real-time feedback on traffic accident conditions and temporary traffic emergencies. In recent years, there has been continuous improvement of road infrastructure and the large-scale erection of urban monitoring systems. Many scholars use machine vision technology to detect road traffic incidents based on video information collected by roadside monitoring equipment [16; 17; 18; 19; 20; 21]. However, machine vision methods have low performance and speed in real-time disease detection due to complex image preprocessing and feature extraction steps. Furthermore, an important disadvantage of such methods is that they are not suitable for real detection scenarios with complex backgrounds. With the increasing application of models represented by deep convolutional neural networks in the direction of computer vision, such as autonomous driving, face recognition, and object detection in industrial scenarios, traffic incident detection methods based on deep learning were also quickly applied. At present, the object detection methods based on deep learning are mainly divided into two categories according to the network structure. One is a one-stage detection method represented by YOLO [22; 23; 24; 25; 26], and the other is represented by R-CNN [27; 28; 29], a two-stage method. The single-stage method directly uses the network to extract recognition features to generate prediction boxes, and the advantage is that the detection speed is fast.