The probability hypothesis density (PHD) filter is a method for tracking multiple target objects based on unlabeled detections. However, as the PHD filter employs a first-order approximation of random finite sets, it does not provide track labels, i.e., targets of consecutive time steps are not associated with each other. In this paper, an intuitive and efficient labeling strategy on top of the extended target PHD filter is proposed. The approach is based on solving a network flow problem and makes use of the Wasserstein metric to account for the spatial extent of the objects. The resulting tracker is evaluated with laser scanner data from two traffic scenarios.

中文翻译：

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扩展目标跟踪PHD过滤器的网络流标签

概率假设密度（PHD）过滤器是一种基于未标记检测来跟踪多个目标对象的方法。但是，由于PHD滤波器采用随机有限集的一阶近似，因此它不提供轨迹标签，即连续时间步长的目标彼此不相关。在本文中，提出了一种在扩展目标PHD滤波器之上的直观，有效的标记策略。该方法基于解决网络流量问题，并利用Wasserstein指标来说明对象的空间范围。使用来自两个交通场景的激光扫描仪数据评估生成的跟踪器。