Multi-object tracking (MOT) is a challenging task that involves detecting objects in the scene and tracking them across a sequence of frames. Evaluating this task is difficult due to temporal occlusions, and varying trajectories across a sequence of images. The main evaluation metric to benchmark MOT methods on datasets such as KITTI has recently become the higher order tracking accuracy (HOTA) metric, which is capable of providing a better description of the performance over metrics such as MOTA, DetA, and IDF1. Point detection and tracking is a closely related task, which could be regarded as a special case of object detection. However, there are differences in evaluating the detection task itself (point distances vs. bounding box overlap). When including the temporal dimension and multi-view scenarios, the evaluation task becomes even more complex. In this work, we propose a multi-view higher order tracking metric (mvHOTA) to determine the accuracy of multi-point (multi-instance and multi-class) detection, while taking into account temporal and spatial associations. mvHOTA can be interpreted as the geometric mean of the detection, association, and correspondence accuracies, thereby providing equal weighting to each of the factors. We demonstrate a use-case through a publicly available endoscopic point detection dataset from a previously organised medical challenge. Furthermore, we compare with other adjusted MOT metrics for this use-case, discuss the properties of mvHOTA, and show how the proposed correspondence accuracy and the Occlusion index facilitate analysis of methods with respect to handling of occlusions. The code will be made publicly available.

本研究提出了一种多视角高阶追踪度量（mvHOTA）来确定多点（多实例和多类）追踪方法的准确性，同时考虑时间和空间关联。我们演示了该指标用于评估内窥镜点检测数据集，并与其他调整的MOT指标进行比较，讨论了mvHOTA的性质，并展示了提议的多视图关联和遮挡指数（OI）如何方便处理遮挡。

mvHOTA: 一种用于多点检测的多视图高阶跟踪精度度量指标，用于测量空间和时间相关性