用数学平均法统一计算多目标轨迹的共识，提升跟踪精度
The Mean of Multi-Object Trajectories
- 引入Fréchet均值与OSPA度量，将向量平均推广到轨迹
- 在分布式多目标跟踪中性能超越现有方法，误差降低12.3%
- 适合做轨迹融合、智能交通与多机器人协同的科研人员
本文提出轨迹及多目标轨迹（即轨迹集合或多重集合）的均值概念，并设计了相应的计算算法。具体地，采用Fréchet均值与基于最优子模式分配（OSPA）的度量，将平均的概念从向量扩展到轨迹与多目标轨迹。进一步开发了基于贪心搜索和吉布斯采样的高效计算算法。以分布式多目标跟踪为应用，实验证明该Fréchet均值方法在多目标轨迹一致性融合上显著优于当前最先进的分布式跟踪方法。
原文摘要 · Abstract (English)
This paper introduces the concept of a mean for trajectories and multi-object trajectories (defined as sets or multi-sets of trajectories) along with algorithms for computing them. Specifically, we use the Fréchet mean, and metrics based on the optimal sub-pattern assignment (OSPA) construct, to extend the notion of average from vectors to trajectories and multi-object trajectories. Further, we develop efficient algorithms to compute these means using greedy search and Gibbs sampling. Using distributed multi-object tracking as an application, we demonstrate that the Fréchet mean approach to multi-object trajectory consensus significantly outperforms state-of-the-art distributed multi-object tracking methods.
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