Computer vision learning techniques for sports video analytics: removing overlays

Arthur C. Depoian II; Lorenzo E Jaques; Dong Xie; Colleen P. Bailey; Parthasarathy Guturu

doi:10.1117/12.2560888

24 April 2020 Computer vision learning techniques for sports video analytics: removing overlays

Arthur C. Depoian II, Lorenzo E Jaques, Dong Xie, Colleen P. Bailey, Parthasarathy Guturu

Proceedings Volume 11395, Big Data II: Learning, Analytics, and Applications; 113950M (2020) https://doi.org/10.1117/12.2560888
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

Abstract

Big data has been driving professional sports over the last decade. In our data-driven world, it becomes important to find additional methods for the analysis of both games and athletes. There is an abundance of videos taken in professional and amateur sports. Player datasets can be created utilizing computer vision techniques. We propose a novel approach by creating an autonomous masking algorithm that can receive live or previously recorded video footage of sporting events. This procedure can identify graphical overlays to optimize further processing by tracking and text recognition algorithms for real-time analysis.

Conference Presentation

Citation Download Citation

Arthur C. Depoian II, Lorenzo E Jaques, Dong Xie, Colleen P. Bailey, and Parthasarathy Guturu "Computer vision learning techniques for sports video analytics: removing overlays", Proc. SPIE 11395, Big Data II: Learning, Analytics, and Applications, 113950M (24 April 2020); https://doi.org/10.1117/12.2560888

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