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Multiple detection joint integrated track splitting for multiple extended target tracking

机译：多重检测联合集成轨道分割，用于多个扩展目标跟踪

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摘要

The extended target tracking problem, which occurs due to a high-resolution sensor resolving multiple scattering points of a target, is considered in this paper. Typical target tracking algorithms which use the point target assumption yield poor performances in extended target tracking scenarios, especially in the presence of closely spaced targets. This performance degeneration is mainly caused by a number of measurements that are shared among tracks when the extended targets are closely spaced. In order to achieve robust tracking performances in multiple extended target tracking environments, a novel tracker, named multiple detection joint integrated track splitting (MD-JITS), is proposed here. This tracker incorporates the measurement cells and the random matrix into a target existence based multitarget track splitting structure. In MD-JITS, each track builds a type of tree, and each branch of the tree is a track component corresponding to a sequence of data associations for multiple scans. The track kinematic state and extension state are obtained on the basis of the track components. Then, the proposed algorithm is tested using both Monte Carlo simulations and a real video surveillance application to demonstrate the effectiveness. (C) 2019 Elsevier B.V. All rights reserved.
机译：本文考虑了由于高分辨率传感器解决了目标的多个散射点而引起的扩展目标跟踪问题。使用点目标假设的典型目标跟踪算法在扩展的目标跟踪方案中（尤其是在存在紧密间隔的目标的情况下）会产生较差的性能。这种性能下降的主要原因是，当扩展目标间隔很近时，在磁道之间共享许多测量结果。为了在多个扩展目标跟踪环境中实现鲁棒的跟踪性能，在此提出了一种新颖的跟踪器，称为多重检测联合集成跟踪拆分（MD-JITS）。该跟踪器将测量单元和随机矩阵合并到基于目标存在的多目标轨道拆分结构中。在MD-JITS中，每个轨道都构建一棵树，并且树的每个分支都是一个轨道组件，与多个扫描的数据关联序列相对应。基于轨道分量获得轨道运动状态和伸展状态。然后，使用蒙特卡洛模拟和真实视频监视应用程序对提出的算法进行测试，以证明其有效性。 （C）2019 Elsevier B.V.保留所有权利。

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