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Vehicle Detection in Aerial Images Using Generic Features, Grouping, and Context

机译:使用通用特征,分组和上下文在航空图像中进行车辆检测

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This paper introduces a new approach on automatic vehicle detection in monocular large scale aerial images. The extraction is based on a hierarchical model that describes the prominent vehicle features on different levels of detail. Besides the object properties, the model comprises also contextual knowledge, i.e., relations between a vehicle and other objects as, e.g., the pavement beside a vehicle and the sun causing a vehicle's shadow projection. In contrast to most of the related work, our approach neither relies on external information like digital maps or site models, nor it is limited to very specific vehicle models. Various examples illustrate the applicability and flexibility of this approach. However, they also show the deficiencies which clearly define the next steps of our future work.
机译:本文介绍了一种在单眼大范围航拍图像中自动检测车辆的新方法。提取基于分层模型,该模型在不同的细节级别上描述了突出的车辆功能。除了对象属性之外,该模型还包括上下文知识,即,车辆与其他对象之间的关系,例如,在车辆旁边的人行道和引起车辆阴影投影的太阳。与大多数相关工作相比,我们的方法既不依赖于外部信息(如数字地图或站点模型),也不限于非常特定的车辆模型。各种示例说明了该方法的适用性和灵活性。但是,它们也显示出明显的缺陷,这些缺陷清楚地定义了我们未来工作的下一步。

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