Abstract
<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">The perception of color in sports teams and branding alike is a highly valued asset in broadcasting and marketing that is worth protecting. The appearance of color is impacted by many factors including capture source, ambient light, and the limitations of screen technology. The current industry practice involves manual adjustment by a technician, who corrects the footage from a dozen or more camera feeds in realtime. This method adjusts the entire video frame based on the producer’s instructions. This paper presents a -pending, machine-learning approach titled “ColorNet” that targets brand color areas of a frame to output a color-corrected feed in realtime. Initial tests of ColorNet demonstrate the ability to reproduce manually created correction masks with high accuracy, while also being computationally efficient. Discussion includes system training and development and plans for a beta test where the system will be applied during a live broadcast of a sporting event in partnership with Clemson University Athletics</i> .
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