Countering Bias in Tracking Evaluations
Gustav Häger, Michael Felsberg, Fahad Shahbaz Khan · 2018
Recent years have witnessed a significant leap in visual object tracking performance mainly due to powerfulfeatures, sophisticated learning methods and the introduction of benchmark datasets. Despite this significantimprovement, the evaluation of state-of-the-art object trackers still relies on the classical intersection overunion (IoU) score. In this work, we argue that the object tracking evaluations based on classical IoU score aresub-optimal. As our first contribution, we theoretically prove that the IoU score is biased in the case of largetarget objects and favors over-estimated target prediction sizes. As our second contribution, we propose a newscore that is unbiased with respect to target prediction size. We systematically evaluate our proposed approachon benchmark tracking data with variations in relative target size. Our empirical results clearly suggest thatthe proposed score is unbiased in general.