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Vehicle Detection from Aerial Images Using Local Shape Information

机译：使用局部形状信息从航拍图像中进行车辆检测

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摘要

Detection and extraction of vehicle objects in high resolution satellite imagery are required in many transportation applications. This paper presents an approach to automatic vehicle detection from aerial images. The initial extraction of candidate vehicle is based on Mean-shift algorithm with symmetric character of blob-like car structure. By fusing the density and the symmetry, the method can remove the ambiguous blobs and reduce the cost of the detected ROI processing in the subsequent stage. To verify the blob as a vehicle, log-polar shape descriptor is used for measuring similarity. The edge strengths are obtained and represented as its spatial histogram by the orientation and distance from the center of blob. The proposed algorithm is able to successfully detect; the vehicle and very useful for the traffic surveillance system.
机译：在许多运输应用中，需要在高分辨率卫星图像中检测和提取车辆对象。本文提出了一种从航空影像中自动检测车辆的方法。候选车辆的初始提取基于具有斑点状汽车结构对称特征的均值漂移算法。通过融合密度和对称性，该方法可以去除歧义斑点，并降低后续阶段检测到的ROI处理的成本。为了验证斑点是否为车辆，对数极坐标形状描述符用于测量相似性。获得边缘强度，并通过方向和距斑点中心的距离将其表示为空间直方图。所提出的算法能够成功检测到；车辆，对于交通监控系统非常有用。

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