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Countering bias in ...
Countering bias in tracking evaluations
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- Häger, Gustav, 1988- (author)
- Linköpings universitet,Datorseende,Tekniska fakulteten
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- Felsberg, Michael, 1974- (author)
- Linköpings universitet,Datorseende,Tekniska fakulteten
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- Khan, Fahad Shahbaz, 1983- (author)
- Linköpings universitet,Datorseende,Tekniska fakulteten
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(creator_code:org_t)
- Science and Technology Publications, Lda, 2018
- 2018
- English.
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In: Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications. - : Science and Technology Publications, Lda. - 9789897582905 ; , s. 581-587
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https://liu.diva-por... (primary) (Raw object)
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https://doi.org/10.5...
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Abstract
Subject headings
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- 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.
Subject headings
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Signalbehandling (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Signal Processing (hsv//eng)
Publication and Content Type
- ref (subject category)
- kon (subject category)
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