Single-Stage Uncertainty-Aware Jersey Number Recognition in Soccer

Prelegent(ci)

mgr Łukasz Grad

Afiliacja

MIMUW

Język referatu

angielski

Termin

30 maja 2025 16:00

Pokój

p. 4060

Link

https://meet.google.com/jbj-tdsr-aop

Seminarium

Seminarium badawcze „Systemy Inteligentne”

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In this talk, I will present a single-stage, uncertainty-aware approach for jersey number recognition in soccer. My method uses digit-compositional classifiers that exploit the structural relationships between digits, integrated with a Dirichlet-based uncertainty modeling framework. I also introduce a tracklet aggregation strategy that combines frame-level predictions using confidence-based filtering. Unlike traditional multi-stage pipelines that rely heavily on explicit jersey detection, I reframe the problem from the perspective of uncertainty quantification. This shift allows for more nuanced and robust predictions, especially in cases of partial visibility or occlusion. My unified architecture processes player crops directly, simplifying the pipeline while enhancing reliability. Through extensive experiments on the SoccerNet and Copa America (CA, my dataset) benchmarks, I show that digit-compositional approaches consistently outperform independent classifiers. Moreover, Dirichlet-based uncertainty modeling yields better-calibrated confidence estimates across varying visibility conditions. On the SoccerNet Challenge benchmark, my method achieves strong results, reaching 85.62% tracklet-level accuracy.

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