内蒙古警官学校内蒙古监狱管理局服刑人员矫治办公室 呼和浩特 010070
TN99
Correctional Office of Inner Mongolia Prison Administration, Inner Mongolia Police School, Hohhot 010070,China
车流量检测在智能交通领域具有重要的地位，车流量检测的重点是识别监控视频中的车辆并计数，因此，提出一种有效的背景建模和准确的车辆计数方法显得尤为重要。本文提出了背景差分法和混合高斯模型来提高运动车辆的检测质量，并在车道内设置了虚拟线圈，利用连通域分析法、区域灰度均值法两种方法检测运动车辆。实验结果表明区域灰度均值法要优于连通域分析法，区域灰度均值法可以解决车辆漏检的问题，车辆计数的准确度有了显著的提高，准确率可达到94%以上。
The vehicle flow detection plays an important role in the field of the intelligent transportation systems(ITS).The vehicle flow detection focuses on detection and counting of the vehicles in the surveillance videos. However, it requires to present a kind of effective background modeling and accurate vehicle counting method.In this paper, we proposed the background subtraction (BS) and the Gaussian mixture model (GMM) for improving the detection quality of the moving vehicles. In addition, we set up the virtual loops for vehicle counting by the connected component analysis (CCA) method and the region gray average values (RAGV) method. The experiment results showed that the RAGV method was superior to the CCA method. The RAGV method could solve the problem of the missing vehicles. The accuracy of vehicle counting could be improved significantly and reach 94% by the RAGV method.
戴晶华,郭俊生.多车道视频车流量检测和计数[J].国外电子测量技术,2016,35(10):30-33
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