Lai A S, Yung N C
Lab. for Intelligent Transp. Syst. Res., Hong Kong Univ., Pokfulam.
IEEE Trans Syst Man Cybern B Cybern. 2000;30(4):539-48. doi: 10.1109/3477.865171.
This paper describes a novel lane detection algorithm for visual traffic surveillance applications under the auspice of intelligent transportation systems. Traditional lane detection methods for vehicle navigation typically use spatial masks to isolate instantaneous lane information from on-vehicle camera images. When surveillance is concerned, complete lane and multiple lane information is essential for tracking vehicles and monitoring lane change frequency from overhead cameras, where traditional methods become inadequate. The algorithm presented in this paper extracts complete multiple lane information by utilizing prominent orientation and length features of lane markings and curb structures to discriminate against other minor features. Essentially, edges are first extracted from the background of a traffic sequence, then thinned and approximated by straight lines. From the resulting set of straight lines, orientation and length discriminations are carried out three-dimensionally with the aid of two-dimensional (2-D) to three-dimensional (3-D) coordinate transformation and K-means clustering. By doing so, edges with strong orientation and length affinity are retained and clustered, while short and isolated edges are eliminated. Overall, the merits of this algorithm are as follows. First, it works well under practical visual surveillance conditions. Second, using K-means for clustering offers a robust approach. Third, the algorithm is efficient as it only requires one image frame to determine the road center lines. Fourth, it computes multiple lane information simultaneously. Fifth, the center lines determined are accurate enough for the intended application.
本文介绍了一种在智能交通系统支持下用于视觉交通监控应用的新型车道检测算法。传统的用于车辆导航的车道检测方法通常使用空间掩码从车载摄像头图像中分离瞬时车道信息。当涉及监控时，完整的车道和多车道信息对于从高架摄像头跟踪车辆和监测车道变换频率至关重要，而传统方法在这方面变得不足。本文提出的算法通过利用车道标线和路缘结构的显著方向和长度特征来区分其他次要特征，从而提取完整的多车道信息。本质上，首先从交通序列的背景中提取边缘，然后将其细化并近似为直线。从得到的直线集合中，借助二维（2-D）到三维（3-D）坐标变换和K均值聚类进行三维方向和长度判别。通过这样做，具有强方向和长度亲和力的边缘被保留并聚类，而短的和孤立的边缘被消除。总体而言，该算法的优点如下。第一，它在实际视觉监控条件下运行良好。第二，使用K均值进行聚类提供了一种稳健的方法。第三，该算法效率高，因为它只需要一帧图像就能确定道路中心线。第四，它能同时计算多个车道信息。第五，确定的中心线对于预期应用来说足够准确。
