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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\u2019s location, ball speed and catcher\u2019s 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.<\/p>","DOI":"10.4018\/jmdem.2010111202","type":"journal-article","created":{"date-parts":[[2010,4,16]],"date-time":"2010-04-16T17:23:54Z","timestamp":1271438634000},"page":"12-36","source":"Crossref","is-referenced-by-count":1,"title":["Automatic Pitch Type Recognition System from Single-View Video Sequences of Baseball Broadcast Videos"],"prefix":"10.4018","volume":"1","author":[{"given":"Masaki","family":"Takahashi","sequence":"first","affiliation":[{"name":"NHK Science and Technology Research Laboratories, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mahito","family":"Fujii","sequence":"additional","affiliation":[{"name":"The Graduate University for Advanced Studies, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Masahiro","family":"Shibata","sequence":"additional","affiliation":[{"name":"NHK Science and Technology Research Laboratories, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nobuyuki","family":"Yagi","sequence":"additional","affiliation":[{"name":"NHK Science and Technology Research Laboratories, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shin\u2019ichi","family":"Satoh","sequence":"additional","affiliation":[{"name":"NHK Science and Technology Research Laboratories, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","reference":[{"key":"jmdem.2010111202-0","unstructured":"Alaways, L. 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