Measuring the Accuracy of Object Detectors and Trackers
Tobias Bottger, Patrick Follmann, Michael Fauser

TL;DR
This paper introduces the relative Intersection over Union (rIoU), a new accuracy measure for object detectors and trackers on densely segmented data, providing more precise evaluation and insights into scene understanding.
Contribution
The paper proposes the rIoU accuracy measure and an evaluation framework, addressing limitations of traditional IoU metrics on densely segmented datasets.
Findings
rIoU provides normalized, more accurate accuracy measurement
The framework is efficient and easy to interpret
Validated on DAVIS and VOT2016 datasets
Abstract
The accuracy of object detectors and trackers is most commonly evaluated by the Intersection over Union (IoU) criterion. To date, most approaches are restricted to axis-aligned or oriented boxes and, as a consequence, many datasets are only labeled with boxes. Nevertheless, axis-aligned or oriented boxes cannot accurately capture an object's shape. To address this, a number of densely segmented datasets has started to emerge in both the object detection and the object tracking communities. However, evaluating the accuracy of object detectors and trackers that are restricted to boxes on densely segmented data is not straightforward. To close this gap, we introduce the relative Intersection over Union (rIoU) accuracy measure. The measure normalizes the IoU with the optimal box for the segmentation to generate an accuracy measure that ranges between 0 and 1 and allows a more precise…
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