论文标题
MOTCOM：多对象跟踪数据集复杂度度量
MOTCOM: The Multi-Object Tracking Dataset Complexity Metric
论文作者
论文摘要
没有全面的度量来描述多对象跟踪（MOT）序列的复杂性。缺乏指标可降低解释性，使数据集的比较变得复杂，并将跟踪器绩效的对话减少到排行榜位置的问题。作为一种补救措施，我们介绍了新型的MOT数据集复杂度度量（MOTCOM），该度量是由MOT关键问题启发的三个子计量学的组合：闭塞，运动不稳定和视觉相似性。 MOTCOM的见解可以开放有关跟踪器性能的细微讨论，并可能导致对鲜为人知的数据集或旨在解决子问题的新颖贡献的更广泛认可。我们在综合MOT17，MOT20和MOTSYNTH数据集上评估了MOTCOM，并表明MOTCOM在描述与传统密度和轨道数量相比描述MOT序列的复杂性要好得多。项目页面https://vap.aau.dk/motcom
There exists no comprehensive metric for describing the complexity of Multi-Object Tracking (MOT) sequences. This lack of metrics decreases explainability, complicates comparison of datasets, and reduces the conversation on tracker performance to a matter of leader board position. As a remedy, we present the novel MOT dataset complexity metric (MOTCOM), which is a combination of three sub-metrics inspired by key problems in MOT: occlusion, erratic motion, and visual similarity. The insights of MOTCOM can open nuanced discussions on tracker performance and may lead to a wider acknowledgement of novel contributions developed for either less known datasets or those aimed at solving sub-problems. We evaluate MOTCOM on the comprehensive MOT17, MOT20, and MOTSynth datasets and show that MOTCOM is far better at describing the complexity of MOT sequences compared to the conventional density and number of tracks. Project page at https://vap.aau.dk/motcom
