TY - GEN
T1 - Offensive Play Recognition of Basketball Video Footage Using ActionFormer
AU - Chiura, Tafadzwa Blessing
AU - van der Haar, Dustin
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - This paper will aim to conduct 3 experiments to determine the best-performing action recognition approach on the SpaceJam dataset. The 3 experiments are Temporal Segment Network (TSN), Inflated 3D-CNN (I3D) and Pose-estimation (Pose-C3D). TSN and I3D yielded similar results with TSN scoring 54.88% for the mean accuracy, 94.33% for top-5 accuracy and 54.88 top-1 accuracy. And I3D scored 53.07% mean accuracy, 91.65% for top-5 accuracy and 53.07 mean accuracy. When Pose-C3d is run for 240 epochs it achieves better results with a top 1 accuracy and mean-class accuracy of 63.15% and a top-5 accuracy of 95.51. These results indicate that the models can distinguish between similar actions such as running and walking in basketball with relative success.
AB - This paper will aim to conduct 3 experiments to determine the best-performing action recognition approach on the SpaceJam dataset. The 3 experiments are Temporal Segment Network (TSN), Inflated 3D-CNN (I3D) and Pose-estimation (Pose-C3D). TSN and I3D yielded similar results with TSN scoring 54.88% for the mean accuracy, 94.33% for top-5 accuracy and 54.88 top-1 accuracy. And I3D scored 53.07% mean accuracy, 91.65% for top-5 accuracy and 53.07 mean accuracy. When Pose-C3d is run for 240 epochs it achieves better results with a top 1 accuracy and mean-class accuracy of 63.15% and a top-5 accuracy of 95.51. These results indicate that the models can distinguish between similar actions such as running and walking in basketball with relative success.
KW - Action Recognition
KW - Basketball
KW - Offensive Play Recognition
UR - http://www.scopus.com/inward/record.url?scp=85171334748&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-35989-7_57
DO - 10.1007/978-3-031-35989-7_57
M3 - Conference contribution
AN - SCOPUS:85171334748
SN - 9783031359880
T3 - Communications in Computer and Information Science
SP - 447
EP - 454
BT - HCI International 2023 Posters - 25th International Conference on Human-Computer Interaction, HCII 2023, Proceedings, Part I
A2 - Stephanidis, Constantine
A2 - Antona, Margherita
A2 - Ntoa, Stavroula
A2 - Salvendy, Gavriel
PB - Springer Science and Business Media Deutschland GmbH
T2 - 25th International Conference on Human-Computer Interaction , HCII 2023
Y2 - 23 July 2023 through 28 July 2023
ER -