提出快速计算多目标跟踪评估指标TGOSPA的新算法。
Fast computation of the TGOSPA metric for multiple target tracking via unbalanced optimal transport
- 将TGOSPA转化为不平衡多边际最优传输问题求解。
- 引入熵正则化，迭代求解对偶问题，显著降低计算耗时。
- 适合需要高效评估大量目标跟踪结果的研究者使用。
在多目标跟踪中，评估算法性能至关重要。轨迹广义最优子模式分配度量（TGOSPA）是一种新提出的评估指标。其计算依赖于优化问题求解，但在大规模跟踪场景下，求解过程计算开销巨大。本文提出一种近似算法，将TGOSPA问题建模为不平衡多边际最优传输问题。基于最近的计算最优传输进展，引入熵正则化，并推导出求解正则化问题对偶的迭代方法。数值实验表明，该算法比使用线性规划求解精确度量更高效，同时仍能提供足够准确的近似结果。
原文摘要 · Abstract (English)
In multiple target tracking, it is important to be able to evaluate the performance of different tracking algorithms. The trajectory generalized optimal sub-pattern assignment metric (TGOSPA) is a recently proposed metric for such evaluations. The TGOSPA metric is computed as the solution to an optimization problem, but for large tracking scenarios, solving this problem becomes computationally demanding. In this paper, we present an approximation algorithm for evaluating the TGOSPA metric, based on casting the TGOSPA problem as an unbalanced multimarginal optimal transport problem. Following recent advances in computational optimal transport, we introduce an entropy regularization and derive an iterative scheme for solving the Lagrangian dual of the regularized problem. Numerical results suggest that our proposed algorithm is more computationally efficient than the alternative of computing the exact metric using a linear programming solver, while still providing an adequate approximation of the metric.
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