提出可评估多目标跟踪不确定性的新指标，更真实反映跟踪性能。
Probabilistic Trajectory GOSPA: A Metric for Uncertainty-Aware Multi-Object Tracking Performance Evaluation
- 基于概率GOSPA扩展，同时考虑目标存在与状态的不确定性
- 线性规划松弛后可在多项式时间内计算，保持可解释性
- 适合需要评估轨迹置信度的自动驾驶、无人机等场景
本文提出一种轨迹通用最优子模式分配（GOSPA）度量的推广形式，用于评估提供轨迹估计及跟踪级别不确定性的多目标跟踪算法。该度量在近期提出的概率GOSPA基础上，同时考虑个体目标状态的存在性与状态估计不确定性。与轨迹GOSPA（TGOSPA）类似，该度量可表述为多维分配问题，其线性规划松弛仍为有效度量，且可在多项式时间内计算。此外，该度量保持了TGOSPA的可解释性，其分解可得到与正确检测目标的期望定位误差、存在概率不匹配误差、期望漏检与虚警误差以及轨迹切换误差相关的直观代价项。通过仿真实验验证了所提度量的有效性。
原文摘要 · Abstract (English)
This paper presents a generalization of the trajectory general optimal sub-pattern assignment (GOSPA) metric for evaluating multi-object tracking algorithms that provide trajectory estimates with track-level uncertainties. This metric builds on the recently introduced probabilistic GOSPA metric to account for both the existence and state estimation uncertainties of individual object states. Similar to trajectory GOSPA (TGOSPA), it can be formulated as a multidimensional assignment problem, and its linear programming relaxation--also a valid metric--is computable in polynomial time. Additionally, this metric retains the interpretability of TGOSPA, and we show that its decomposition yields intuitive costs terms associated to expected localization error and existence probability mismatch error for properly detected objects, expected missed and false detection error, and track switch error. The effectiveness of the proposed metric is demonstrated through a simulation study.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。