发表机构
Institute for Imaging, Data and Communications (IDCOM), University of Edinburgh; Department of Engineering, University of Cambridge; School of Mathematics, University of Edinburgh（爱丁堡大学成像、数据与通信研究所（IDCOM）; 剑桥大学工程系; 爱丁堡大学数学学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对数据稀缺雷达应用中多目标检测跟踪难题，PiVoT通过联合推断目标多方面信息，无需外部聚类或检测器，利用变分推断创新实现快速抗杂波跟踪，实验证明其在多方面性能出色，优于现有贝叶斯跟踪器。
AI 中文摘要
在许多数据稀缺的雷达应用中，从噪声点云进行多目标检测和跟踪仍然具有挑战性。当前基于泊松测量模型的贝叶斯跟踪器提供了一种无需训练的解决方案，但在严重杂波、大量目标和全分辨率多普勒点云情况下，难以实现准确性和效率。我们使用PiVoT来解决这个问题，它是一种用于位置和多普勒测量的快速、抗杂波多目标跟踪器。PiVoT通过联合推断目标状态、形状、存在概率、数据关联和测量率，对大量且随时间变化的目标进行端到端检测和跟踪，无需外部聚类或检测器。其效率得益于多种变分推断创新，如理论上合理的出生剪枝算法、精确更新的二次到线性复杂度降低以及计算高效的多普勒泊松模型。实验表明，PiVoT在具有挑战性的场景中大大优于现有的贝叶斯跟踪器，同时还展示了对一千个目标的出色可扩展性、对与目标视觉上无法分离的杂波的鲁棒性，以及在全尺寸现代汽车雷达数据集上的实时操作能力，在无需训练的联合检测器和跟踪器方面，其性能可与深度学习检测基准相媲美。
英文摘要
Multi-object detection and tracking from noisy point clouds remain challenging in many data-scarce radar applications. Current Bayesian trackers based on Poisson measurement models offer a training-free solution but struggle to achieve accuracy and efficiency under severe clutter, large object populations, and full-resolution Doppler point clouds. We address this with PiVoT, a fast, clutter-resilient multi-object tracker for both positional and Doppler measurements. PiVoT performs end-to-end detection and tracking of a large and time-varying number of objects without external clustering or detectors, through joint inference of object states, shapes, existence probabilities, data association, and measurement rates. Its efficiency is driven by several variational inference innovations, such as theoretically justified birth pruning, quadratic-to-linear complexity reductions for exact updates, and a computationally efficient Doppler Poisson model. Experiments show that PiVoT substantially outperforms existing Bayesian trackers in challenging scenes, while also demonstrating exceptional scalability to a thousand objects, robustness to clutter visually inseparable from objects, and real-time operation on full-scale modern automotive radar datasets, where it attains performance comparable to a deep-learning detection benchmark as a training-free joint detector and tracker.
