  Multi-point tracking is a challenging task that involves detecting points in
the scene and tracking them across a sequence of frames. Computing
detection-based measures like the F-measure on a frame-by-frame basis is not
sufficient to assess the overall performance, as it does not interpret
performance in the temporal domain. The main evaluation metric available comes
from Multi-object tracking (MOT) methods to benchmark performance on datasets
such as KITTI with the recently proposed 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. While the HOTA metric takes into
account temporal associations, it does not provide a tailored means to analyse
the spatial associations of a dataset in a multi-camera setup. Moreover, there
are differences in evaluating the detection task for points when compared to
objects (point distances vs. bounding box overlap). Therefore 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) tracking methods,
while taking into account temporal and spatial associations.mvHOTA can be
interpreted as the geometric mean of detection, temporal, and spatial
associations, thereby providing equal weighting to each of the factors. We
demonstrate the use of this metric to evaluate the tracking performance on an
endoscopic point detection dataset from a previously organised surgical data
science challenge. Furthermore, we compare with other adjusted MOT metrics for
this use-case, discuss the properties of mvHOTA, and show how the proposed
multi-view Association and the Occlusion index (OI) facilitate analysis of
methods with respect to handling of occlusions. The code is available at
https://github.com/Cardio-AI/mvhota.
