Estimating the trajectories of multi-objects poses a significant challenge due to data association ambiguity, which leads to a substantial increase in computational requirements. To address such problems, a divide-and-conquer manner has been employed with parallel computation. In this strategy, distinguished objects that have unique labels are grouped based on their statistical dependencies, the intersection of predicted measurements. Several geometry approaches have been used for label grouping since finding all intersected label pairs is clearly infeasible for large-scale tracking problems. This paper proposes an efficient implementation of label grouping for label-partitioned generalized labeled multi-Bernoulli filter framework using a secondary partitioning technique. This allows for parallel computation in the label graph indexing step, avoiding generating and eliminating duplicate comparisons. Additionally, we compare the performance of the proposed technique with several efficient spatial searching algorithms. The results demonstrate the superior performance of the proposed approach on large-scale data sets, enabling scalable trajectory estimation.

翻译：多目标轨迹估计因数据关联模糊性而面临重大挑战，导致计算需求显著增长。为解决此类问题，现有方法采用分治策略结合并行计算实现。在该策略中，根据预测量测交集所反映的统计依赖性，对具有唯一标签的差异化目标进行分组。由于在大规模跟踪问题中寻找所有相交标签对显然不可行，已有多种几何方法被用于标签分组。本文提出一种基于二次分区技术的标签分组高效实现方案，用于标签分区广义标签多伯努利滤波器框架。该方法可在标签图索引阶段实现并行计算，避免了重复比较的生成与消除。此外，我们将所提技术与多种高效空间搜索算法进行性能比较。实验结果表明，所提方法在大规模数据集上展现出优越性能，实现了可扩展的轨迹估计。