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A Method for Player Importance Prediction from Player Network Using Gaze Position Estimated by LSTM

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Title: A Method for Player Importance Prediction from Player Network Using Gaze Position Estimated by LSTM
Authors: Suzuki, Genki Browse this author
Takahashi, Sho Browse this author →KAKEN DB
Ogawa, Takahiro Browse this author →KAKEN DB
Haseyama, Miki Browse this author →KAKEN DB
Keywords: Sports video analysis
tactical analysis
first-arrival region
link analysis
gaze tracking data
long short-term memory
Issue Date: 25-Aug-2020
Publisher: The Institute of Image Information and Television Engineers
Journal Title: ITE Transactions on Media Technology and Applications
Volume: 8
Issue: 3
Start Page: 151
End Page: 160
Publisher DOI: 10.3169/mta.8.151
Abstract: A novel method for player importance prediction from a player network using gaze positions estimated by Long Short-Term Memory (LSTM) in soccer videos is presented in this paper. By newly using an estimation model of gaze positions trained by gaze tracking data of experienced persons, it is expected that the importance of each player can be predicted. First, we generate a player network by utilizing the estimated gaze positions and first-arrival regions representing players' connections, e.g., passes between players. The gaze positions are estimated by LSTM that is newly trained from the gaze tracking data of experienced persons. Second, the proposed method predicts the importance of each player by applying the Hypertext Induced Topic Selection (HITS) algorithm to the constructed network. Consequently, prediction of the importance of each player based on soccer tactic knowledge of experienced persons can be realized without constantly obtaining gaze tracking data.
Type: article
URI: http://hdl.handle.net/2115/79107
Appears in Collections:情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

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