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A robust geometric model of road extraction method for intelligent traffic system

机译：智能交通系统的道路提取方法强大的几何模型

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In the intelligent transportation system, the geometry for the street is an important factor in vehicle monitoring. It helps to point out areas of interest, reduce computing costs, increased accuracy in detecting and identifying objects and facilitate data collection. In this paper, a new robust method of extracting the geometric model of the road is presented. The method is based on vehicle motion data to show the exact shape of the street without depending on the edge elements. First, the special features of consecutive frames are extracted and matched together. Second, by stretching the lines from matching these respective keypoints, intersections are indicated. The vanishing point is achieved by calculating the center of these intersections. Third, combining the infinity and the extremes of the motion data to tangent to the boundary of the geometry. Finally, by charting the area with the greatest matching ratio between the keypoints in the adjacent frame we reach the area of interest. Vietnam traffic dataset is used to verify the effectiveness and accuracy of the proposed method. Experimental results show that the proposed method has shown the correct geometry of the path. The use of motion data is a sustainable method of extracting Vanishing Point without being affected by other side effects.
机译：在智能交通系统中，街道的几何形状是车辆监测的重要因素。它有助于指出感兴趣的区域，降低计算成本，提高检测和识别对象的准确性，并促进数据收集。本文提出了一种提取道路几何模型的新鲁棒方法。该方法基于车辆运动数据，以显示街道的精确形状，而无需取决于边缘元件。首先，将连续帧的特殊功能提取并匹配在一起。其次，通过拉伸从匹配这些相应的关键点的线路，指示交叉点。通过计算这些交叉点的​​中心来实现消失点。第三，将动作数据的无限和极端与几何边界相结合。最后，通过绘制相邻帧中的关键点之间具有最大匹配比的区域，我们达到感兴趣的区域。越南流量数据集用于验证所提出的方法的有效性和准确性。实验结果表明，该方法显示了路径的正确几何形状。运动数据的使用是一种在不受其他副作用的情况下提取消失点的可持续方法。

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