基于视频的夜间高速公路车辆事件检测

Highway Vehicle Detection at Night Base on Video

针对高速公路夜间行驶车辆的特点,基于最优化理论提出了一种鲁棒的车辆检测和跟踪算法,对现有的车灯提取算法和轨迹跟踪规则进行了改进,不仅可自动统计和显示车流量,车速等交通信息,并且能对逆行、拥堵、自由流停车等交通车辆事件做出自动判断。实验结果表明,该算法复杂性低,实时性好,在夜间路况较好的条件下车辆检测成功率达95%以上,在拥挤交通条件下,检测正确率在80%左右。

In the video detection system of highway traffic flow, it is difficult to detect vehicles. This paper studies nighttime highway traffic vehicles and proposes a robust vehicle detection and tracking algorithm based on optimization theory. The proposed algorithm improves the previous methods for headlight detection and the rules for trajectory tracking. At the same time, it can not only automatically present traffic flow and vehicles speed statistically, but also recognize traffic vehicle event such as jam-packed or driving against the traffic. Experiment results demonstrate the algorithm has lower complexity and better performance than other methods. The detection rate can reach up to 95% or so, robust with low complexity, real-time feature and its detection ratio reaches up to 95% in smooth traffic conditions and 80% in traffic jams.