A Method for Player Importance Prediction from Player Network Using Gaze Position Estimated by LSTM
| Autor: | Takahiro Ogawa, Genki Suzuki, Miki Haseyama, Sho Takahashi |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Computer science
business.industry Sports video analysis tactical analysis first-arrival region Computer Graphics and Computer-Aided Design Gaze link analysis Long short term memory Position (vector) Signal Processing Media Technology gaze tracking data Computer vision Artificial intelligence business long short-term memory Link analysis |
| Zdroj: | ITE Transactions on Media Technology and Applications. 8(3):151-160 |
| ISSN: | 2186-7364 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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