Extracting Player Tracking Data from Video Using Non-Stationary Cameras and a Combination of Computer Vision Techniques

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Abstract

Neil Johnson, Sports Analytics Developer, ESPN

Abstract

This research paper looks at the feasibility of parsing player tracking data from a single non-stationary camera like the one typically used in sports broadcasts. Parsing player tracking data from broadcast video opens up a plethora of applications that allows the technology to be scaled downwards and across sports in addition to capturing events that could otherwise not be captured. This approach uses an array of open source computer vision applications including pose estimation and template matching. Early tests show the accuracy of this new method in placing players within a foot of their true location at 94.5%. Making player tracking data more accessible lowers the barrier of entry and increases the timespan for which advanced methods of analysis can be practiced. Additionally, the pose estimation data itself provides an additional new frontier of data analysis that can increase the fidelity of analysis that relies of player tracking data.

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