  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.
