TY - GEN
T1 - 3D global trajectory and multi-view local motion combined player action recognition in volleyball analysis
AU - Liu, Yang
AU - Huang, Shuyi
AU - Cheng, Xina
AU - Ikenaga, Takeshi
N1 - Funding Information:
Acknowledgment. This work was supported by KAKENHI (16K13006) and Waseda University Grant for Special Research Projects (2018K-302).
Publisher Copyright:
© Springer Nature Switzerland AG 2018.
PY - 2018
Y1 - 2018
N2 - Volleyball video analysis is important for developing applications such as player evaluation system or tactic analysis system. Among its different topics, player action recognition serves as an elementary building brick for understanding player’s behavior. Most conventional player action recognition methods have limits in real volleyball game due to the occlusion and intra-class variation problems. This paper proposes a 3D global trajectory and multi-view local motion combined volleyball player action recognition method. 3D global trajectory extracts global motion feature through 3D trajectories, which hides the unstable and incomplete 2D motion feature caused by the above problems. Multi-view local motion gets detailed local motion feature of arms and legs in multiple viewpoints and removes clutter features caused by occlusion problem. Through the combination, global 3D feature and local motion feature mutually promote each other and the actions are recognized well. Experiments are conducted on game videos from the Semifinal and Final Game of 2014 Japan Inter High School Games of Men’s Volleyball in Tokyo Metropolitan Gymnasium. The experiments show the combing result accuracy achieves 98.39%, 95.50%, 96.86%, 96.98% for spike, block, receive, toss respectively and improve 11.33% averagely than the sing-view local motion based result.
AB - Volleyball video analysis is important for developing applications such as player evaluation system or tactic analysis system. Among its different topics, player action recognition serves as an elementary building brick for understanding player’s behavior. Most conventional player action recognition methods have limits in real volleyball game due to the occlusion and intra-class variation problems. This paper proposes a 3D global trajectory and multi-view local motion combined volleyball player action recognition method. 3D global trajectory extracts global motion feature through 3D trajectories, which hides the unstable and incomplete 2D motion feature caused by the above problems. Multi-view local motion gets detailed local motion feature of arms and legs in multiple viewpoints and removes clutter features caused by occlusion problem. Through the combination, global 3D feature and local motion feature mutually promote each other and the actions are recognized well. Experiments are conducted on game videos from the Semifinal and Final Game of 2014 Japan Inter High School Games of Men’s Volleyball in Tokyo Metropolitan Gymnasium. The experiments show the combing result accuracy achieves 98.39%, 95.50%, 96.86%, 96.98% for spike, block, receive, toss respectively and improve 11.33% averagely than the sing-view local motion based result.
KW - Intra-class variation
KW - Occlusion
KW - Player action recognition
KW - Volleyball analysis
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U2 - 10.1007/978-3-030-00764-5_13
DO - 10.1007/978-3-030-00764-5_13
M3 - Conference contribution
AN - SCOPUS:85054492166
SN - 9783030007638
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 134
EP - 144
BT - Advances in Multimedia Information Processing – PCM 2018 - 19th Pacific-Rim Conference on Multimedia, 2018, Proceedings
A2 - Ngo, Chong-Wah
A2 - Yamasaki, Toshihiko
A2 - Hong, Richang
A2 - Wang, Meng
A2 - Cheng, Wen-Huang
PB - Springer Verlag
T2 - 19th Pacific-Rim Conference on Multimedia, PCM 2018
Y2 - 21 September 2018 through 22 September 2018
ER -