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A robust and efficient framework for sports-field registration

We propose a novel framework to register sports-fields as they appear in broadcast sports videos. Unlike previous approaches, we particularly address the challenge of field registration when: (a) there are not enough distinguishable features on the field, and (b) no prior knowledge is available about the camera. To this end, we detect a grid of keypoints distributed uniformly on the entire field instead of using only sparse local corners and line intersections, thereby extending the keypoint coverage to the texture-less parts of the field as well. To further improve keypoint based homography estimate, we differentialbly warp and align it with a set of dense field-features defined as normalized distancemap of pixels to their nearest lines and key-regions. We predict the keypoints and dense field-features simultaneously using a multi-task deep network to achieve computational efficiency. To have a comprehensive evaluation, we have compiled a new dataset called SportsFields which is collected from 192 video-clips from 5 different sports covering large environmental and camera variations. We empirically demonstrate that our algorithm not only achieves state of the art field-registration accuracy but also runs in real-time for HD resolution videos using commodity hardware.

For the full paper, see A robust and efficient framework for sports-field registration on the Amazon Science website.

Senior Applied Scientist – Amazon Devices
Applied Scientist II – Prime Video
Senior Principal Scientist – Prime Video