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A Metric for Performance Evaluation of Multi-Target Tracking Algorithms

机译:多目标跟踪算法的性能评估指标

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摘要

Performance evaluation of multi-target tracking algorithms is of great practical importance in the design, parameter optimization and comparison of tracking systems. The goal of performance evaluation is to measure the distance between two sets of tracks: the ground truth tracks and the set of estimated tracks. This paper proposes a mathematically rigorous metric for this purpose. The basis of the proposed distance measure is the recently formulated consistent metric for performance evaluation of multi-target filters, referred to as the OSPA metric. Multi-target filters sequentially estimate the number of targets and their position in the state space. The OSPA metric is therefore defined on the space of finite sets of vectors. The distinction between filtering and tracking is that tracking algorithms output tracks and a track represents a labeled temporal sequence of state estimates, associated with the same target. The metric proposed in this paper is therefore defined on the space of finite sets of tracks and incorporates the labeling error. Numerical examples demonstrate that the proposed metric behaves in a manner consistent with our expectations.
机译:多目标跟踪算法的性能评估在跟踪系统的设计,参数优化和比较中具有重要的现实意义。性能评估的目的是测量两组轨迹之间的距离:地面真实轨迹和一组估算轨迹。为此,本文提出了一个严格的数学度量。提议的距离度量的基础是最近制定的用于多目标滤波器性能评估的一致性度量,称为OSPA度量。多目标过滤器顺序估计目标的数量及其在状态空间中的位置。因此,OSPA度量标准是在有限的向量集空间上定义的。过滤和跟踪之间的区别在于,跟踪算法输出跟踪,并且跟踪表示与同一目标关联的状态估计的标记时间序列。因此,本文提出的度量标准是在有限的轨道集空间上定义的,并结合了标记误差。数值示例表明,提出的度量标准的行为符合我们的预期。

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