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.

翻译：本文提出了两种用于评估多目标跟踪（MOT）算法性能的准度量方法。第一种准度量是广义最优子模式分配（GOSPA）度量的扩展，用于衡量目标集合之间的差异；第二种准度量是轨迹GOSPA（T-GOSPA）度量的扩展，用于衡量轨迹集合之间的差异。与基于GOSPA的度量类似，这些准度量包含对正确检测目标的定位误差、虚警目标数量以及漏检目标数量的代价项。其中T-GOSPA准度量还额外引入了轨迹切换代价。区别于GOSPA和T-GOSPA度量，本文提出的准度量具备灵活调整漏检与虚警惩罚权重的特性，且无需满足定位代价的对称性要求。我们进一步阐释了基于这些准度量构建相似度评分函数的方法。通过仿真实验，采用T-GOSPA准度量对多种贝叶斯MOT算法的性能进行了评估。