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Segmentation method of moving vehicles based on semi-fuzzy cluster

机译:基于半模糊聚类的运动车辆分割方法

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摘要

Moving vehicles segmentation is the most fundamental and vital problem in the intelligent transportation systems. This paper proposes a moving vehicles segmentation method that combines the semi-fuzzy cluster algorithm under the guidance of edge-based information with the traditional background subtraction algorithm. Above all, the current frame image that involves moving vehicles is divided into two parts using the algorithm of edge detection and edge closing. One part is the set of edge pixels and the other one is the regions encircled by the edge pixels. And then, every edge pixel will be associated into the most reasonable region according to the semi-fuzzy cluster algorithm. At last, the regions that similar with the background will be deleted and the remained regions are the moving vehicles in the current frame. Simulation experiments show that the new method posed in this paper is more robust and exact, and have a high ability of anti noise together with a high application value.
机译:移动车辆分割是智能交通系统中最基本,最关键的问题。提出了一种基于边缘信息的半模糊聚类算法与传统背景减法相结合的运动车辆分割方法。最重要的是,使用边缘检测和边缘闭合算法将涉及移动车辆的当前帧图像分为两部分。一部分是边缘像素的集合,另一部分是边缘像素包围的区域。然后,根据半模糊聚类算法,将每个边缘像素关联到最合理的区域。最后,与背景相似的区域将被删除,其余区域是当前帧中的移动车辆。仿真实验表明,本文提出的新方法更加鲁棒和精确,具有较高的抗噪声能力和较高的应用价值。

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