This paper introduces temporally local metrics for Multi-Object Tracking. These metrics are obtained by restricting existing metrics based on track matching to a finite temporal horizon, and provide new insight into the ability of trackers to maintain identity over time. Moreover, the horizon parameter offers a novel, meaningful mechanism by which to define the relative importance of detection and association, a common dilemma in applications where imperfect association is tolerable. It is shown that the historical Average Tracking Accuracy (ATA) metric exhibits superior sensitivity to association, enabling its proposed local variant, ALTA, to capture a wide range of characteristics. In particular, ALTA is better equipped to identify advances in association independent of detection. The paper further presents an error decomposition for ATA that reveals the impact of four distinct error types and is equally applicable to ALTA. The diagnostic capabilities of ALTA are demonstrated on the MOT 2017 and Waymo Open Dataset benchmarks.

翻译：本文介绍了多物体跟踪的当地时间指标。这些指标是通过限制基于与有限时间范围相匹配的轨迹的现有指标获得的,并对跟踪者在一段时间内保持身份的能力提供了新的洞察力。此外,地平线参数提供了一个新的、有意义的机制,用以界定探测和关联的相对重要性,这是在不完美的关联可以容忍的应用程序中常见的两难处境。这表明历史平均跟踪准确性(ATA)指标显示对关联的高度敏感性,使得其拟议的本地变量ALTA能够捕捉广泛的特征。特别是,ALTA更有能力识别关联性的进展,而无需检测。该文件还进一步介绍了ATA的错误分解位置,揭示了四种不同错误类型的影响,并同样适用于ALTA。ALTA的诊断能力在MOT 2017 和Waymo 开放数据集基准上得到了演示。