Multiobject tracking provides situational awareness that enables new applications for modern convenience, public safety, and homeland security. This paper presents a factor graph formulation and a particle-based sum-product algorithm (SPA) for scalable detection and tracking of extended objects. The proposed method dynamically introduces states of newly detected objects, efficiently performs probabilistic multiple-measurement to object association, and jointly infers the geometric shapes of objects. Scalable extended object tracking (EOT) is enabled by modeling association uncertainty by measurement-oriented association variables and newly detected objects by a Poisson birth process. Contrary to conventional EOT methods, a fully particle-based approach makes it possible to describe different geometric object shapes. The proposed method can reliably detect, localize, and track a large number of closely-spaced extended objects without gating and clustering of measurements. We demonstrate significant performance advantages of our approach compared to the recently introduced Poisson multi-Bernoulli mixture filter. In particular, we consider a simulated scenarios with up to twenty closely-spaced objects and a real autonomous driving application where measurements are captured by a lidar sensor.

中文翻译：

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几何扩展对象的可扩展检测和跟踪


多对象跟踪提供态势感知，可实现现代便利、公共安全和国土安全的新应用。本文提出了一种因子图公式和一种基于粒子的和积算法（SPA），用于扩展对象的可扩展检测和跟踪。该方法动态引入新检测到的物体的状态，有效地对物体关联进行概率多重测量，并联合推断物体的几何形状。可扩展的扩展对象跟踪（EOT）是通过面向测量的关联变量和泊松诞生过程新检测到的对象对关联不确定性进行建模来实现的。与传统的 EOT 方法相反，完全基于粒子的方法可以描述不同的几何对象形状。所提出的方法可以可靠地检测、定位和跟踪大量紧密间隔的扩展对象，而无需对测量进行门控和聚类。与最近推出的泊松多伯努利混合滤波器相比，我们展示了我们的方法的显着性能优势。特别是，我们考虑了具有多达 20 个紧密间隔物体的模拟场景以及由激光雷达传感器捕获测量结果的真实自动驾驶应用。