Abstract
The problem of tracking multiple objects has been investigated in various research and industrial fields. Among existing methods, random finite set (RFS) solutions such as the generalized labeled multi-Bernoulli (GLMB) filter has provided efficient solutions with solid theoretical justifications. Furthermore, implementations show that the GLMB approach is efficient under challenging scenarios. In this paper, we study an RFS-based method for multi-object tracking (MOT) through a simple data structure for label partitioning. Specifically, grid index structure based techniques for splitting a label space and a label-partitioned GLMB tracker are investigated. We finally evaluate the performance of label partitioning and the GLMB filter via various means such as visualization, execution time, and MOT metrics.
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