# Research on Urban Traffic Incident Detection Based on Vehicle Cameras
## 1 Introduction  
As the economy continues to develop, both in urban and rural areas, the number of traffic participants increases significantly, resulting in traffic congestion. Therefore, traffic control and management strategies need to be developed to alleviate frequent and unusual traffic congestion [1]. Frequent congestion refers to predictable situations where demand exceeds the capacity of the transportation system, such as during a daily rush hour. Extreme traffic congestion is often caused by unpredictable accidents such as traffic accidents, crowd gatherings, fires, and weather-related problems. It is very important to provide drivers and traffic managers with traffic situation information in real time and efficiently for severe congestion [2]. Then, for city managers, it is of great significance to sense and deal with abnormal traffic incidents in the city in time to prevent the launch of serious incidents, and this can also improve the timely feedback and rescue efficiency of relevant departments for abnormal incidents [3]. On the other hand, urban traffic anomaly detection is also important for improving people's quality of life. For example, traffic congestion is now the biggest headache in most big cities [4]. Serious traffic jams will bring a lot of economic losses and destroy people's moods. If most traffic jams in a city can be detected at an early stage, by informing people of traffic conditions in advance, people can further avoid traffic jams from becoming serious by changing their travel plans or travel patterns [5].  
For a long time in the past, manual detection was the main method of urban road traffic incident detection to a certain extent, such as the use of surveillance video manual inspection, highway patrol cars, and telephone alarm methods to monitor traffic incidents.  
The manual inspection method is time-consuming and labor-intensive, the detection efficiency is low, the accuracy rate is not high, and road inspections generally take place on long roads, so the interval between patrols at the same location is long, and traffic incidents cannot be detected in time. With the development of automatic monitoring technology, many areas have deployed automatic detection technology for roadside surveillance equipment, and such technology has effectively improved the efficiency of event detection. However, the detection task is still limited by the installation density of roadside devices; it is impossible to install roadside devices in every place. Therefore, roadside surveillance devices always have blind spots, and it is difficult to detect and deal with traffic events occurring in the blind spots at the first instance.  
In summary, existing traffic event detectors are difficult to use in all directions because the equipment cannot be moved. In addition, nowadays, more and more cameras of smart vehicles, as well as the driving recorders installed in traditional cars, can be our potential traffic event detectors. In this study, we propose to use the incoming image data from cameras installed on smart vehicles for edge-side event detection, an approach that can effectively increase the range and improve the granularity of urban traffic event sensing. This solves the problem of traditional traffic event detection devices often being installed on roadside devices, which are limited by the installation density of the devices and make it difficult to detect road events in all directions.  
The rest of the paper is structured as follows. Section 2 presents the related work; Section 3 describes and explains the proposed method; Section 4 will reveal and discuss the proposed model and results; Section 5 analyzes and discusses the results; finally, Section 6 summarizes the work of this paper.