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Automatic Pitch Type Recognition System from Single-View Video Sequences of Baseball Broadcast Videos

Automatic Pitch Type Recognition System from Single-View Video Sequences of Baseball Broadcast Videos

Masaki Takahashi, Mahito Fujii, Masahiro Shibata, Nobuyuki Yagi, Shin’ichi Satoh
Copyright: © 2010 |Volume: 1 |Issue: 1 |Pages: 25
ISSN: 1947-8534|EISSN: 1947-8542|ISSN: 1947-8534|EISBN13: 9781616929954|EISSN: 1947-8542|DOI: 10.4018/jmdem.2010111202
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MLA

Takahashi, Masaki, et al. "Automatic Pitch Type Recognition System from Single-View Video Sequences of Baseball Broadcast Videos." IJMDEM vol.1, no.1 2010: pp.12-36. http://doi.org/10.4018/jmdem.2010111202

APA

Takahashi, M., Fujii, M., Shibata, M., Yagi, N., & Satoh, S. (2010). Automatic Pitch Type Recognition System from Single-View Video Sequences of Baseball Broadcast Videos. International Journal of Multimedia Data Engineering and Management (IJMDEM), 1(1), 12-36. http://doi.org/10.4018/jmdem.2010111202

Chicago

Takahashi, Masaki, et al. "Automatic Pitch Type Recognition System from Single-View Video Sequences of Baseball Broadcast Videos," International Journal of Multimedia Data Engineering and Management (IJMDEM) 1, no.1: 12-36. http://doi.org/10.4018/jmdem.2010111202

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Abstract

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.

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