Biblio

The incidence of abnormal road traffic events, especially abnormal traffic congestion, is becoming more and more prominent in daily traffic management in China. It has become the main research work of urban traffic management to detect and identify traffic congestion incidents in time. Efficient and accurate detection of traffic congestion incidents can provide a good strategy for traffic management. At present, the detection and recognition of traffic congestion events mainly rely on the integration of road traffic flow data and the passing data collected by electronic police or devices of checkpoint, and then estimating and forecasting road conditions through the method of big data analysis; Such methods often have some disadvantages such as low time-effect, low precision and small prediction range. Therefore, with the help of the current large and medium cities in the public security, traffic police have built video surveillance equipment, through computer vision technology to analyze the traffic flow from video monitoring, in this paper, the motion state and the changing trend of vehicle flow are obtained by using the technology of vehicle detection from video and multi-target tracking based on deep learning, so as to realize the perception and recognition of traffic congestion. The method achieves the recognition accuracy of less than 60 seconds in real-time, more than 80% in detection rate of congestion event and more than 82.5% in accuracy of detection. At the same time, it breaks through the restriction of traditional big data prediction, such as traffic flow data, truck pass data and GPS floating car data, and enlarges the scene and scope of detection.

Currently, video surveillance systems play an important role in ensuring the safety of citizens, their property, etc., which greatly contributes to the reduction of crime. Due to the high intrinsic value and/or high efficiency of their use for the prevention and detection of crimes, they themselves often become the objects of illegal actions (theft, damage). The main purpose of video surveillance systems is to provide continuous visual monitoring of the situation at a particular facility or territory, as well as event registration. The breakdown of the camera is detected by the loss of signal in the control center. However, the absence of a signal for reasons other than these can also be caused by an accident on the power line, a communication channel break, software or hardware breakdown of the camera itself. In this regard, there is a problem of determining the exact cause of the lack of signal and, consequently, the need for a rapid response to it. The paper proposes an approach of video surveillance arrangement according to their main functional purpose, as well as their ability to monitor each other. Based on this approach, a mathematical model of the choice of locations and conditions of location of video surveillance equipment from a set of potentially acceptable as a problem of nonlinear Boolean programming is developed. This model maximizes the functionality of the video surveillance system, taking into account the importance of areas and objects of surveillance with restrictions on the number of video surveillance of each type, the nature of the terrain and existing buildings. An algorithm for solving this problem is proposed.