提出新型多目标追踪评估指标，可灵活调整误检与漏检惩罚。
GOSPA and T-GOSPA quasi-metrics for evaluation of multi-object tracking algorithms
- 扩展GOSPA和T-GOSPA，引入可调惩罚权重的准度量
- 支持非对称定位误差、不同误检/漏检成本，更贴近实际场景
- 适合需要精细评估追踪算法性能的研究者使用
本文提出两种用于多目标追踪（MOT）算法评估的准度量。一种是广义最优子模式分配（GOSPA）的扩展，衡量对象集合间的差异；另一种是轨迹GOSPA（T-GOSPA）的扩展，衡量轨迹集合间的差异。与基于GOSPA的度量类似，这些准度量包含正确检测对象的定位误差、误检数和漏检数的成本。T-GOSPA准度量还引入了轨迹切换成本。与传统GOSPA和T-GOSPA不同，所提准度量允许对误检和漏检设置不同惩罚权重，且定位成本无需对称。文中还介绍了如何基于这些准度量构建相似性评分函数。通过仿真，评估了几种贝叶斯MOT算法在T-GOSPA准度量下的表现。
原文摘要 · Abstract (English)
This paper introduces two quasi-metrics for performance assessment of multi-object tracking (MOT) algorithms. One quasi-metric is an extension of the generalised optimal subpattern assignment (GOSPA) metric and measures the discrepancy between sets of objects. The other quasi-metric is an extension of the trajectory GOSPA (T-GOSPA) metric and measures the discrepancy between sets of trajectories. Similar to the GOSPA-based metrics, these quasi-metrics include costs for localisation error for properly detected objects, the number of false objects and the number of missed objects. The T-GOSPA quasi-metric also includes a track switching cost. Differently from the GOSPA and T-GOSPA metrics, the proposed quasi-metrics have the flexibility of penalising missed and false objects with different costs, and the localisation costs are not required to be symmetric. We also explain how to obtain similarity score functions based on these quasi-metrics. The performance of several Bayesian MOT algorithms is assessed with the T-GOSPA quasi-metric via simulations.
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