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".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
A aquisição automática de dados de estratégia de jogo é importante para a realização de sistemas profissionais de análise de estratégia, fornecendo valores de avaliação como o status da equipe e a eficácia das jogadas. O principal fator que influencia o desempenho da aquisição de dados estratégicos no jogo de voleibol são os papéis desconhecidos dos jogadores. Papel do jogador significa a posição com significado de jogo de cada jogador na formação da equipe, como levantador, atacante e bloqueador. O papel desconhecido do jogador torna o jogador individual pouco confiável e perde a contribuição de cada jogador na análise da estratégia. Este artigo propõe um recurso de movimento de equipe divisional de quadra e uma curva de desempenho do jogador para lidar com os papéis desconhecidos dos jogadores na aquisição de dados estratégicos. Primeiramente, o recurso de movimento da equipe divisional da quadra é proposto para a detecção do status tático da equipe. Este recurso reduz a influência das informações individuais do jogador, somando a densidade de movimento relativo da bola de todos os jogadores na área de quadra dividida, que corresponde às diferentes jogadas. Em segundo lugar, são propostas as curvas de desempenho dos jogadores para a aquisição de variáveis de eficácia no jogo de ataque. Os candidatos aos papéis dos jogadores são detectados por três características que representam todo o processo de um jogador começar a correr (ou pular) para a bola e acertá-la: a distância relativa da bola, o movimento de aproximação da bola e a característica do movimento de ataque. Com as trajetórias da bola em 3D e as posições de vários jogadores rastreadas a partir de vídeos de jogos de vôlei com visualização múltipla, a taxa de detecção experimental do status de cada equipe (ataque, pronto para defesa, pronto para ataque e status de ataque) é de 75.2%, 84.2%, 79.7% e 81.6%. E para a aquisição das variáveis de eficácia de ataque, a precisão média da zona definida, o número de atacantes disponíveis, o ritmo de ataque e o número de bloqueadores são 100%, 100%, 97.8% e 100%, que atingem uma melhoria média de 8.3%. em comparação com a aquisição manual.
Xina CHENG
Waseda University
Takeshi IKENAGA
Waseda University
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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
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@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},}
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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 -