目的 为解决车辆对车道标记的遮挡问题,提出一种新的背景提取算法,同时基于透视变换实现了城市交叉路口的多车道标定。方法 首先,通过均值与帧间差分方法的融合,进行城市交叉路口的背景稳定与更新;然后,利用Canny算子及Hough直线检测得到各类直线;其次,基于透视变换、聚类分析和先验知识建立了车道线的筛选数学模型,实现了车道线标定;最后,通过实验对算法进行了验证。结果 采用10min长度、分辨率为2592×2048像素的某城市交叉路口实际监控视频进行交叉路口背景提取。本文算法的背景提取准确率比均值法和传统高斯混合模型法分别提升20%和30%左右,车道线标定也优于其他类似方法。结论 算法具有收敛速度快、准确率较高、稳定性较好等特点,在车流量大时可快速更新并消除车辆虚影,适用于光照条件正常的城市交叉种口的车道线标定。

Objective Inductive loop sensors are commonly used to detect traffic violations. However, these tools are expensive and difficult to maintain. Tracing vehicles or detecting violation by video analysis represents an alternative way to exploit computer vision. Thus, the calibration of intersection driveways, which is the basic task of the intelligent transportation system in the detecting traffic violations, must be primarily addressed. This paper presents a solution for extracting intersection backgrounds and marking out driveways. Method First, we propose a new background extraction algorithm, which inherits the features of both mean and frame difference methods, to solve the problem of lane markers that are partly covered by vehicles. On the one hand, this algorithm exploits the average image to estimate an extra multiplying power that keeps the background relatively stable. On the other hand, this algorithm calculates the frame difference and uses such difference to update the background progressively. Thus, the proposed algorithm achieves fast convergence when the traffic runs smoothly and can be quickly updated during traffic hours. Second, several lines are detected by employing Canny and Hough. Based on perspective transformation, clustering analysis, and prior knowledge, a filtering mathematical model is proposed to detect the driveway from these lines. Third, the proposed algorithm is verified by conducting experiments. Result The proposed algorithm can obtain a more robust outcome than the Gaussian mixture model with five Gaussian distributions, which is one of the most widely used background extraction methods. Using the manual background as ground truth, the proposed algorithm can quantitatively compare the gray value of the ground truth's pixel with the corresponding extracted background's pixel. The result of the experiments shows that the accuracy rate of background extraction is 20% and 30% more than that of the mean method and the traditional Gaussian mixed model, respectively. According to the cycle data of traffic lights, the ghost can be avoided when the vehicles stop at red light. In other words, the proposed algorithm can distinguish between a temporary stopped objective and a long-time stationary background. Similarly, the calibration method can precisely determine the pseudo lane lines using a clustering or filtering strategy and produce a reliable result. Conclusion The proposed algorithm has several merits, such as fast convergence rate, higher accuracy rate, and excellent stability, which can rapidly erase the virtual shadow of the vehicle. In addition, calibration can be accomplished in the daytime as a one-time effort. Thus, the proposed algorithm is suitable to calibrate driveways with normal lights. The experiments also demonstrate the effectiveness and practicality of the proposed method. However, the algorithm still requires further optimization and analysis in adjusting parameters. In the future, we will attempt to devise a new method to select the suitable parameters adaptively by adopting some machine learning approaches.