New York: Scientists at Disney Research Lab, Pittsburgh, have developed robotic cameras that mimic human operators to anticipate basketball game action and learn how to better frame shots of a game.
Peter Carr, a Disney Research engineer, and Jianhui Chen, Ph.D. student in computer science at the University of British Columbia, devised a data-driven approach that allows a camera system to monitor an expert camera operator during a basketball game.
The automated system uses machine learning algorithms to recognise the relationship between player locations and corresponding camera configurations.
"We do not use any direct information about the ball's location because tracking the ball with a single camera is difficult," Carr said.
"But players are coached to be in the right place at the right time, so their formations usually give strong clues about the ball's location," he added.
Carr and Chen demonstrated their system on a high school basketball game.
They used two cameras - a broadcast camera that was operated by a human expert and another that was a stationary camera that the computer used to detect and track the players automatically.
Following supervised learning based on the operator's actions, the system was able to predict how to pan the camera in a way that was superior to the best previous algorithm and that did indeed mimic a human operator.
"The method can be adapted to other sports possibly with additional features," Carr noted.
Future work will also include mimicking the auxiliary cameras used for cutaway shots in multi-camera productions.
The team was scheduled to report their findings at "WACV 2015", the IEEE Winter Conference on Applications of Computer Vision at Waikoloa Beach, Hawaii this week.