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Countering bias in tracking evaluations
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-6096-3648
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
2018 (English)In: Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications / [ed] Francisco Imai, Alain Tremeau and Jose Braz, Science and Technology Publications, Lda , 2018, Vol. 5, p. 581-587Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
Science and Technology Publications, Lda , 2018. Vol. 5, p. 581-587
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:liu:diva-151306DOI: 10.5220/0006714805810587ISBN: 9789897582905 (print)OAI: oai:DiVA.org:liu-151306DiVA, id: diva2:1248643
Conference
13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, January 27-29, Funchal, Madeira
Available from: 2018-09-17 Created: 2018-09-17 Last updated: 2019-06-26Bibliographically approved

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Countering bias in tracking evaluations(2131 kB)18 downloads
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Häger, GustavFelsberg, MichaelKhan, Fahad Shahbaz

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