TY - GEN
T1 - Action Recognition using Time-series Heat Maps of Joint Positions from Volleyball Match Videos
AU - Kondo, Akimasa
AU - Saito, Hideo
AU - Yachida, Shoji
AU - Fujiwara, Ryo
N1 - Publisher Copyright:
© 2022 ACM.
PY - 2022/10/14
Y1 - 2022/10/14
N2 - Data analysis in sports is becoming increasingly important, and one of the sports in which sports analysts play an active role is volleyball. Volleyball analysts have the task of annotating match videos, a time-consuming and technically challenging task that makes use of data difficult. In this paper, we propose a method for recognizing players' actions from volleyball game videos using time-series heat maps of joint positions to automate the analysis of volleyball match videos. In experiments to verify the effectiveness of the proposed method, we confirmed that the use of time-series heat maps of joint positions improves both the accuracy and F1 score compared to the baseline method using only RGB images as an input. We also confirmed the effectiveness of the proposed method in recognizing players' actions from volleyball match videos, which were not included in the dataset.
AB - Data analysis in sports is becoming increasingly important, and one of the sports in which sports analysts play an active role is volleyball. Volleyball analysts have the task of annotating match videos, a time-consuming and technically challenging task that makes use of data difficult. In this paper, we propose a method for recognizing players' actions from volleyball game videos using time-series heat maps of joint positions to automate the analysis of volleyball match videos. In experiments to verify the effectiveness of the proposed method, we confirmed that the use of time-series heat maps of joint positions improves both the accuracy and F1 score compared to the baseline method using only RGB images as an input. We also confirmed the effectiveness of the proposed method in recognizing players' actions from volleyball match videos, which were not included in the dataset.
KW - action recognition
KW - pose estimation
KW - spatio-Temporal convolutions
KW - volleyball
UR - http://www.scopus.com/inward/record.url?scp=85141378725&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85141378725&partnerID=8YFLogxK
U2 - 10.1145/3552437.3555694
DO - 10.1145/3552437.3555694
M3 - Conference contribution
AN - SCOPUS:85141378725
T3 - MMSports 2022 - Proceedings of the 5th International ACM Workshop on Multimedia Content Analysis in Sports
SP - 39
EP - 45
BT - MMSports 2022 - Proceedings of the 5th International ACM Workshop on Multimedia Content Analysis in Sports
PB - Association for Computing Machinery, Inc
T2 - 5th ACM International Workshop on Multimedia Content Analysis in Sports, MMSports 2022, co-located with ACM Multimedia 2022
Y2 - 14 October 2022
ER -