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Multiple detection joint integrated track splitting for multiple extended target tracking

机译:多种检测联合集成轨道分裂,用于多扩展目标跟踪

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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:在本文中考虑了由于高分辨率传感器解决了目标的高分辨率传感器而发生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目标跟踪场景中产生差的性能,特别是在存在紧密间隔的目标中。这种性能变性主要由延伸目标紧密间隔时在轨道之间共享的多项测量引起的。为了在多个扩展目标跟踪环境中实现稳健的跟踪性能,这里提出了一种名为多个检测联合集成轨道分割(MD-JITS)的新型跟踪器。该跟踪器将测量单元和随机矩阵包含到基于目标存在的多标轨道分裂结构中。在MD-JITS中,每个曲目构建一种类型的树,树的每个分支是对应于多个扫描的数据关联序列的轨道组件。基于轨道部件获得轨道运动状态和延伸状态。然后,使用蒙特卡罗模拟和真实视频监控应用来测试所提出的算法,以证明效果。 (c)2019 Elsevier B.v.保留所有权利。

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