April 17, 2024, 1:53 p.m. | ML@CMU

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TL;DR: Off-the-shelf text spotting and re-identification models fail in basic off-road racing settings, even more so during muddy events. Making matters worse, there aren’t any public datasets to evaluate or improve models in this domain. To this end, we introduce datasets, benchmarks, and methods for the challenging off-road racing setting. In the dynamic world of sports analytics, machine learning (ML) systems play a pivotal role, transforming vast arrays of visual data into actionable insights. These systems are adept at navigating …

articles basic benchmarks beyond computer computer vision datasets deep dive domain events identification making public racing text vision

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