Affiliations: [a]
School of Mechanical and Electrical Engineering, Shenzhen Polytechnic, Shenzhen, Guangdong, China
| [b]
School of Artificial Intelligence, Shenzhen Polytechnic, Shenzhen, Guangdong, China
| [c] Garden Center, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou, Guangdong, China
Correspondence:
[*]
Corresponding author. Peng Wang, Garden Center, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou, Guangdong, China. E-mail: [email protected].
Abstract: All along, the identification of night-driving safety car features is a major research direction in the field of intelligent traffic management, with a wide range of applications and development space, and these identification technologies include theoretical knowledge and important theoretical research in many fields. Due to the interference of lights and other light sources, the gray value of the background area also changes frequently. A common method during the day is to detect these background areas as moving vehicles, which greatly reduces the detection accuracy. The most reliable information at night is the headlights. If the light can be accurately detected and other sources are excluded, accurate detection can be performed and tracking accuracy can be guaranteed. Vehicle safety assisted driving technology is one of the main research directions of intelligent transportation systems. It mainly uses computer technology, sensor technology and communication technology to collect and analyze the state information of roads, other vehicles and drivers. Provide advice and warnings to the driver before reaching the danger, determine current traffic conditions and avoid traffic accidents in advance. This paper studies some problems of night vehicle target recognition and detection, mainly the division of target and background, and the classification and recognition of target extraction. To solve these problems, a particle filter algorithm is introduced to introduce nonlinear statistics. The fuzzy theory is introduced to classify the video processed by the particle filter algorithm. The target recognition is realized by the method, and the purpose of identifying the night vehicle target is achieved. Comparative experimental analysis shows that this method is more accurate and powerful than the common target recognition algorithm and can be applied to real scenes.
