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Improving multiple target tracking in structured environments using velocity priors

机译:使用速度先验改进结构化环境中的多目标跟踪

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In this paper, we present an algorithm for determining a velocity probability distribution prior from low frame rate aerial video of an urban area, and show how this may be used to aid in the multiple target tracking problem, as well as to provide a foundation for the automated classification of urban transportation infrastructure. The algorithm used to develop the prior is based on using a generic interest point detector to find automobile candidate locations, followed by a series of filters based on scale and motion to reduce the number of false alarms. The remaining locations are then associated between frame pairs using a simple matching algorithm, and the corresponding tracks are then used to build up velocity histograms in the areas that are moved through between the track endpoints. The algorithm is tested on a dataset taken over urban Tucson, AZ. The results demonstrate that the velocity probability distribution prior can be used to infer a variety of information about road lane directions, speed limits, etc…, as well as providing a means of describing environmental knowledge about traffic rules that can be used in tracking.
机译:在本文中,我们提出了一种从市区的低帧频航拍视频确定速度概率分布的算法,并说明了如何将其用于解决多目标跟踪问题,并为城市交通基础设施的自动化分类。用于开发先验算法的算法是基于使用通用兴趣点检测器来查找汽车候选位置,然后使用一系列基于比例和运动的过滤器以减少错误警报的数量。然后使用简单的匹配算法在帧对之间将其余位置关联起来,然后使用相应的轨迹在轨迹端点之间移动通过的区域中建立速度直方图。该算法在亚利桑那州图森市的数据集上进行了测试。结果表明,速度概率分布先验可用于推断有关车道方向,速度限制等的各种信息,并提供描述可用于跟踪的交通规则的环境知识的手段。

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