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In recent years, with the growth of China's economy and the development of the automobile manufacturing industry, the number of various vehicles has continuously increased, and the incidence of traffic accidents has also increased. Especially in traffic blind areas, right-turning areas of vehicles, etc., traffic accidents such as vehicle collisions are extremely easy to occur, which poses a serious threat to people's lives and property, and is extremely harmful. Therefore, related research on collision detection of people and vehicles has been traffic-safe and has received extensive attention from field researchers. At present, the research on human-vehicle collision detection is to detect human-vehicle collision accidents by tracking the track of vehicles and pedestrians, but there are problems such as poor tracking effect, low accuracy of collision discrimination and complex algorithms. Aiming at these problems, this paper studies the human-vehicle collision detection algorithm based on image processing. Through the image processing of traffic monitoring video, the vehicle and pedestrian contour information is extracted. Based on this, a mathematical model for collision detection is constructed to realize human-vehicle collision detection. The results show that the proposed method can effectively distinguish the collision between pedestrians and vehicles, and the algorithm for image processing is simpler than the traditional tracking algorithm, and the time is shorter. The results show that the image-based collision detection algorithm based on image processing can effectively and quickly identify the traffic accidents in which people and vehicles collide, and then can issue alarm signals in time, shortening the accident processing time and reducing the accident time. The possibility of a secondary accident has a high practicability in the detection of traffic accidents in which people and vehicles collide. [ABSTRACT FROM AUTHOR]

In view of the multipeak characteristics of a photovoltaic (PV) array P–V curve under local shadow conditions and that the traditional maximum power point tracking (MPPT) algorithm cannot effectively track the maximum power point of the curve, a multipeak MPPT algorithm based on a chaotic quantum bee colony and support vector regression (SVR) is proposed. By constructing and analyzing the mathematical model of a photovoltaic array under a local shadow, the P–V characteristic equation of the photovoltaic array is obtained. The improved strategy of the artificial bee colony algorithm is studied, and the improved chaotic quantum bee colony algorithm (CQABC) is applied to the optimization of SVR parameters; this application improves the accuracy and generalization performance of the maximum power point prediction model based on SVR. The calculation process of the multipeak MPPT algorithm based on CQABC-SVR is given, and the effectiveness of the algorithm is verified by simulation and testing. The experimental results show that the algorithm can accurately track the global maximum power point under uniform illumination or local shadow conditions, effectively overcoming the problem of traditional MPPT algorithms easily falling into local extrema. [ABSTRACT FROM AUTHOR]