TY - GEN
T1 - A Crowd Behavior Analysis Method for Large-Scale Performances
AU - Zhang, Qian
AU - Huang, Tianyu
AU - Li, Yihao
AU - Li, Peng
N1 - Publisher Copyright:
© 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2024
Y1 - 2024
N2 - This study combines visual and athletic information to analyze crowd performance, using performance density entropy and performance consistency as visual descriptors and group collectivity as an athletic descriptor. We used these descriptors to develop a crowd performance behavior classification algorithm that can distinguish between different behaviors in large-scale performances. The study found that the descriptors were weakly correlated, indicating that they capture different dimensions of performance. The crowd behavior classification experiments showed that the descriptors were valid for qualitative analysis and consistent with human perception. The proposed algorithm successfully differentiated and described performance behavior in the dataset of a large-scale crowd performance and was demonstrated to be effective.
AB - This study combines visual and athletic information to analyze crowd performance, using performance density entropy and performance consistency as visual descriptors and group collectivity as an athletic descriptor. We used these descriptors to develop a crowd performance behavior classification algorithm that can distinguish between different behaviors in large-scale performances. The study found that the descriptors were weakly correlated, indicating that they capture different dimensions of performance. The crowd behavior classification experiments showed that the descriptors were valid for qualitative analysis and consistent with human perception. The proposed algorithm successfully differentiated and described performance behavior in the dataset of a large-scale crowd performance and was demonstrated to be effective.
KW - Crowd Behavior analysis
KW - Crowd descriptors
KW - Large-scale crowd performances
UR - http://www.scopus.com/inward/record.url?scp=85180737708&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-50078-7_5
DO - 10.1007/978-3-031-50078-7_5
M3 - Conference contribution
AN - SCOPUS:85180737708
SN - 9783031500770
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 54
EP - 66
BT - Advances in Computer Graphics - 40th Computer Graphics International Conference, CGI 2023, Proceedings
A2 - Sheng, Bin
A2 - Bi, Lei
A2 - Kim, Jinman
A2 - Magnenat-Thalmann, Nadia
A2 - Thalmann, Daniel
PB - Springer Science and Business Media Deutschland GmbH
T2 - 40th Computer Graphics International Conference, CGI 2023
Y2 - 28 August 2023 through 1 September 2023
ER -