Multi-object tracking algorithms are deployed in various applications, each with unique performance requirements. For example, track switches pose significant challenges for offline scene understanding, as they hinder the accuracy of data interpretation. Conversely, in online surveillance applications, their impact is often minimal. This disparity underscores the need for application-specific performance evaluations that are both simple and mathematically sound. The trajectory generalized optimal sub-pattern assignment (TGOSPA) metric offers a principled approach to evaluate multi-object tracking performance. It accounts for localization errors, the number of missed and false objects, and the number of track switches, providing a comprehensive assessment framework. This paper illustrates the effective use of the TGOSPA metric in computer vision tasks, addressing challenges posed by the need for application-specific scoring methodologies. By exploring the TGOSPA parameter selection, we enable users to compare, comprehend, and optimize the performance of algorithms tailored for specific tasks, such as target tracking and training of detector or re-ID modules.

翻译：多目标跟踪算法部署于各类应用场景，各场景对算法性能存在独特要求。例如，轨迹跳变在离线场景理解中构成显著挑战，因其会阻碍数据解释的准确性；相反，在在线监控应用中，其影响通常微乎其微。这种差异性凸显了针对特定应用场景进行性能评估的必要性，此类评估需兼具简洁性与数学严谨性。轨迹广义最优子模式分配（TGOSPA）度量提供了一种评估多目标跟踪性能的原则性方法。该度量综合考虑定位误差、漏检目标数、虚警目标数以及轨迹跳变次数，构建了全面的评估框架。本文阐述了TGOSPA度量在计算机视觉任务中的有效运用，以应对特定应用场景评分方法需求带来的挑战。通过探索TGOSPA参数选择机制，我们使用户能够针对特定任务（如目标跟踪、检测器或重识别模块训练）比较、理解并优化算法性能。