The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Pemerolehan data strategi permainan automatik adalah penting untuk merealisasikan sistem analisis strategi profesional dengan menyediakan nilai penilaian seperti status pasukan dan keberkesanan permainan. Faktor utama yang mempengaruhi prestasi pemerolehan data strategi dalam permainan bola tampar ialah peranan pemain yang tidak diketahui. Peranan pemain bermaksud kedudukan dengan makna permainan setiap pemain dalam pembentukan pasukan, seperti setter, penyerang dan penyekat. Peranan pemain yang tidak diketahui menjadikan pemain individu tidak boleh dipercayai dan kehilangan sumbangan setiap pemain dalam analisis strategi. Kertas kerja ini mencadangkan ciri gerakan pasukan bahagian mahkamah dan keluk prestasi pemain untuk menangani peranan pemain yang tidak diketahui dalam pemerolehan data strategi. Pertama, ciri gerakan pasukan bahagian mahkamah dicadangkan untuk pengesanan status taktikal pasukan. Ciri ini mengurangkan pengaruh maklumat pemain individu dengan merumuskan kepadatan gerakan relatif bola semua pemain di kawasan gelanggang yang dibahagikan, yang sepadan dengan permainan yang berbeza. Kedua, keluk prestasi pemain dicadangkan untuk pemerolehan pembolehubah keberkesanan dalam permainan serangan. Calon peranan pemain dikesan oleh tiga ciri yang mewakili keseluruhan proses pemain mula meluru (atau melompat) ke bola dan memukul bola: jarak relatif bola, gerakan menghampiri bola dan ciri gerakan serangan. Dengan trajektori bola 3D dan kedudukan berbilang pemain yang dijejaki daripada video permainan bola tampar berbilang tontonan, kadar pengesanan percubaan bagi setiap status pasukan (status serangan, sedia bertahan, sedia menyerang dan serangan) ialah 75.2%, 84.2%, 79.7% dan 81.6%. Dan untuk pemerolehan pembolehubah keberkesanan serangan, ketepatan purata zon yang ditetapkan, bilangan penyerang yang tersedia, tempo serangan dan bilangan penyekat adalah 100%, 100%, 97.8%, dan 100%, yang mencapai peningkatan purata 8.3% berbanding dengan pemerolehan manual.
Xina CHENG
Waseda University
Takeshi IKENAGA
Waseda University
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Salinan
Xina CHENG, Takeshi IKENAGA, "Court-Divisional Team Motion and Player Performance Curve Based Automatic Game Strategy Data Acquisition for Volleyball Analysis" in IEICE TRANSACTIONS on Fundamentals,
vol. E101-A, no. 11, pp. 1756-1765, November 2018, doi: 10.1587/transfun.E101.A.1756.
Abstract: Automatic game strategy data acquisition is important for the realization of the professional strategy analysis systems by providing evaluation values such as the team status and the efficacy of plays. The key factor that influences the performance of the strategy data acquisition in volleyball game is the unknown player roles. Player role means the position with game meaning of each player in the team formation, such as the setter, attacker and blocker. The unknown player role makes individual player unreliable and loses the contribution of each player in the strategy analysis. This paper proposes a court-divisional team motion feature and a player performance curve to deal with the unknown player roles in strategy data acquisition. Firstly, the court-divisional team motion feature is proposed for the team tactical status detection. This feature reduces the influence of individual player information by summing up the ball relative motion density of all the players in divided court area, which corresponds to the different plays. Secondly, the player performance curves are proposed for the efficacy variables acquisition in attack play. The player roles candidates are detected by three features that represent the entire process of a player starting to rush (or jump) to the ball and hit the ball: the ball relative distance, ball approach motion and the attack motion feature. With the 3D ball trajectories and multiple players' positions tracked from multi-view volleyball game videos, the experimental detection rate of each team status (attack, defense-ready, offense-ready and offense status) are 75.2%, 84.2%, 79.7% and 81.6%. And for the attack efficacy variables acquisition, the average precision of the set zone, the number of available attackers, the attack tempo and the number of blockers are 100%, 100%, 97.8%, and 100%, which achieve 8.3% average improvement compared with manual acquisition.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E101.A.1756/_p
Salinan
@ARTICLE{e101-a_11_1756,
author={Xina CHENG, Takeshi IKENAGA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Court-Divisional Team Motion and Player Performance Curve Based Automatic Game Strategy Data Acquisition for Volleyball Analysis},
year={2018},
volume={E101-A},
number={11},
pages={1756-1765},
abstract={Automatic game strategy data acquisition is important for the realization of the professional strategy analysis systems by providing evaluation values such as the team status and the efficacy of plays. The key factor that influences the performance of the strategy data acquisition in volleyball game is the unknown player roles. Player role means the position with game meaning of each player in the team formation, such as the setter, attacker and blocker. The unknown player role makes individual player unreliable and loses the contribution of each player in the strategy analysis. This paper proposes a court-divisional team motion feature and a player performance curve to deal with the unknown player roles in strategy data acquisition. Firstly, the court-divisional team motion feature is proposed for the team tactical status detection. This feature reduces the influence of individual player information by summing up the ball relative motion density of all the players in divided court area, which corresponds to the different plays. Secondly, the player performance curves are proposed for the efficacy variables acquisition in attack play. The player roles candidates are detected by three features that represent the entire process of a player starting to rush (or jump) to the ball and hit the ball: the ball relative distance, ball approach motion and the attack motion feature. With the 3D ball trajectories and multiple players' positions tracked from multi-view volleyball game videos, the experimental detection rate of each team status (attack, defense-ready, offense-ready and offense status) are 75.2%, 84.2%, 79.7% and 81.6%. And for the attack efficacy variables acquisition, the average precision of the set zone, the number of available attackers, the attack tempo and the number of blockers are 100%, 100%, 97.8%, and 100%, which achieve 8.3% average improvement compared with manual acquisition.},
keywords={},
doi={10.1587/transfun.E101.A.1756},
ISSN={1745-1337},
month={November},}
Salinan
TY - JOUR
TI - Court-Divisional Team Motion and Player Performance Curve Based Automatic Game Strategy Data Acquisition for Volleyball Analysis
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1756
EP - 1765
AU - Xina CHENG
AU - Takeshi IKENAGA
PY - 2018
DO - 10.1587/transfun.E101.A.1756
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E101-A
IS - 11
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - November 2018
AB - Automatic game strategy data acquisition is important for the realization of the professional strategy analysis systems by providing evaluation values such as the team status and the efficacy of plays. The key factor that influences the performance of the strategy data acquisition in volleyball game is the unknown player roles. Player role means the position with game meaning of each player in the team formation, such as the setter, attacker and blocker. The unknown player role makes individual player unreliable and loses the contribution of each player in the strategy analysis. This paper proposes a court-divisional team motion feature and a player performance curve to deal with the unknown player roles in strategy data acquisition. Firstly, the court-divisional team motion feature is proposed for the team tactical status detection. This feature reduces the influence of individual player information by summing up the ball relative motion density of all the players in divided court area, which corresponds to the different plays. Secondly, the player performance curves are proposed for the efficacy variables acquisition in attack play. The player roles candidates are detected by three features that represent the entire process of a player starting to rush (or jump) to the ball and hit the ball: the ball relative distance, ball approach motion and the attack motion feature. With the 3D ball trajectories and multiple players' positions tracked from multi-view volleyball game videos, the experimental detection rate of each team status (attack, defense-ready, offense-ready and offense status) are 75.2%, 84.2%, 79.7% and 81.6%. And for the attack efficacy variables acquisition, the average precision of the set zone, the number of available attackers, the attack tempo and the number of blockers are 100%, 100%, 97.8%, and 100%, which achieve 8.3% average improvement compared with manual acquisition.
ER -