TY - GEN
T1 - Football Penalty Kick Prediction Model Based on Kicker's Pose Estimation
AU - Mauricio Salazar, Josue Angel
AU - Alatrista-Salas, Hugo
N1 - Publisher Copyright:
© 2024 ACM.
PY - 2024/5/24
Y1 - 2024/5/24
N2 - This paper describes an innovative methodology for predicting penalty kicks in football based on the pose estimation of the kicker. Our proposal starts with the construction of a corpus of penalty kick videos. Then, we combine semantic video segmentation and 3D pose estimation using the TAM and MMPose methods. For the prediction of the goal area where the kick would land, three deep-learning models were compared, as well as the study of the part of the player's body that affects the prediction task.
AB - This paper describes an innovative methodology for predicting penalty kicks in football based on the pose estimation of the kicker. Our proposal starts with the construction of a corpus of penalty kick videos. Then, we combine semantic video segmentation and 3D pose estimation using the TAM and MMPose methods. For the prediction of the goal area where the kick would land, three deep-learning models were compared, as well as the study of the part of the player's body that affects the prediction task.
KW - Computer Vision
KW - Football
KW - Human Pose Estimation
KW - Video Segmentation
UR - http://www.scopus.com/inward/record.url?scp=85204677529&partnerID=8YFLogxK
U2 - 10.1145/3674029.3674061
DO - 10.1145/3674029.3674061
M3 - Conference contribution
AN - SCOPUS:85204677529
T3 - ACM International Conference Proceeding Series
SP - 196
EP - 203
BT - Proceedings of the 2024 9th International Conference on Machine Learning Technologies, ICMLT 2024
PB - Association for Computing Machinery
T2 - 9th International Conference on Machine Learning Technologies, ICMLT 2024
Y2 - 24 May 2024 through 26 May 2024
ER -