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SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
A multi-camera method for three-dimensional digitization of dynamic, real-world events
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Pitching a baseball: tracking high-speed motion with multi-exposure images
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ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 1
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PCM'05 Proceedings of the 6th Pacific-Rim conference on Advances in Multimedia Information Processing - Volume Part I
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This article describes a system that automatically recognizes individual pitch types like screwballs and sliders in baseball broadcast videos. These decisions are currently made by human specialists in baseball, who are watching the broadcast video of the game. No automatic system has yet been developed for identifying individual pitch types from single view camera images. Techniques using multiple fixed cameras promise highly accurate pitch type identification, but the systems tend to be large. Our system is designed to identify the same pitch types using only the same single-view broadcast baseball videos used by the human specialists, and accordingly we used a number of features, such as the ball's location, ball speed and catcher's stance based on the advice of those specialists. The system identifies the pitch type using a classifier trained with the Random Forests ensemble learning algorithm and achieved about 90% recognition accuracy in experiments.