TY - GEN
T1 - Action Recognition in Sports Video Considering Location Information
AU - Ichige, Rina
AU - Aoki, Yoshimitsu
PY - 2020
Y1 - 2020
N2 - The purpose of this study is to develop a tactics analysis system using image recognition for rugby. With the Rugby World Cup in 2019 and the Tokyo Olympics in 2020, demand for sports video analysis is increasing. Rugby has more complicated play such as dense play than other sports, and the ball is hidden between players, making it difficult to track. By developing a high-precision analysis technology for rugby with few research cases, we thought that it could be used for other sports and industrial fields other than sports. In this research, we propose a method that adds spatial information to time-series information as a new feature. Using the coordinates obtained by projectively transforming the match video onto the bird’s-eye view image, play classification was performed using the player position, the ball position, and the dense area position as feature amounts. Also, in order to further improve the detection accuracy of the boundaries between plays, attention was paid to the positional relationship of each player on the field.
AB - The purpose of this study is to develop a tactics analysis system using image recognition for rugby. With the Rugby World Cup in 2019 and the Tokyo Olympics in 2020, demand for sports video analysis is increasing. Rugby has more complicated play such as dense play than other sports, and the ball is hidden between players, making it difficult to track. By developing a high-precision analysis technology for rugby with few research cases, we thought that it could be used for other sports and industrial fields other than sports. In this research, we propose a method that adds spatial information to time-series information as a new feature. Using the coordinates obtained by projectively transforming the match video onto the bird’s-eye view image, play classification was performed using the player position, the ball position, and the dense area position as feature amounts. Also, in order to further improve the detection accuracy of the boundaries between plays, attention was paid to the positional relationship of each player on the field.
KW - Dense play
KW - Heatmap features
KW - Subdivision of play area
UR - http://www.scopus.com/inward/record.url?scp=85090032557&partnerID=8YFLogxK
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U2 - 10.1007/978-981-15-4818-5_12
DO - 10.1007/978-981-15-4818-5_12
M3 - Conference contribution
AN - SCOPUS:85090032557
SN - 9789811548178
T3 - Communications in Computer and Information Science
SP - 150
EP - 164
BT - Frontiers of Computer Vision - 26th International Workshop, IW-FCV 2020, Revised Selected Papers
A2 - Ohyama, Wataru
A2 - Jung, Soon Ki
PB - Springer
T2 - International Workshop on Frontiers of Computer Vision, IW-FCV 2020
Y2 - 20 February 2020 through 22 February 2020
ER -