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The system was more successful in basketball games than in soccer games because soccer players tend to hold their formation and their movements thus provide less information about the ball and where the camera should look, Carr said.
In addition to Carr, Hoang Le, Andrew Kang and Yisong Yue of Caltech along with Jianhui Chen and James J.
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"This is the sort of progress we need to realize the huge potential for automated broadcasts of sports and other live events." Current optical tracking technology can't reliably follow the ball automatically for the duration of a match, but the automated camera system can follow the general flow of the game by studying detected player positions.
Because of imperfect sensing, automated cameras generate jittery footage—especially when incorrectly anticipating how the game is about to unfold.
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An automated camera system was able to learn how to better film basketball and soccer games - and smoothly recover from mistakes - by watching human camera operators, scientists at Disney Research report.
The result was footage without much of the jerkiness that plagues automated cameras, said Peter Carr, senior research engineer at Disney Research.
Human operators tend to be a bit more aggressive in following the action, Carr noted, though there were times in their field tests when the camera system outperformed the humans.
In one fast break during a basketball game, for instance, the human operator moved the camera in anticipation of a dunk, while the computer correctly assessed the player positions and predicted a pass play.
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