A Crowd Behavior Analysis Method for Large-Scale Performances

Qian Zhang; Tianyu Huang; Yihao Li; Peng Li

doi:10.1007/978-3-031-50078-7_5

A Crowd Behavior Analysis Method for Large-Scale Performances

Qian Zhang, Tianyu Huang, Yihao Li, Peng Li^*

^*Corresponding author for this work

School of Computer Science and Technology

Beijing Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

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.

Original language	English
Title of host publication	Advances in Computer Graphics - 40th Computer Graphics International Conference, CGI 2023, Proceedings
Editors	Bin Sheng, Lei Bi, Jinman Kim, Nadia Magnenat-Thalmann, Daniel Thalmann
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	54-66
Number of pages	13
ISBN (Print)	9783031500770
DOIs	https://doi.org/10.1007/978-3-031-50078-7_5
Publication status	Published - 2024
Event	40th Computer Graphics International Conference, CGI 2023 - Shanghai, China Duration: 28 Aug 2023 → 1 Sept 2023

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	14498 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	40th Computer Graphics International Conference, CGI 2023
Country/Territory	China
City	Shanghai
Period	28/08/23 → 1/09/23

Keywords

Crowd Behavior analysis
Crowd descriptors
Large-scale crowd performances

Access to Document

10.1007/978-3-031-50078-7_5

Cite this

Zhang, Q., Huang, T., Li, Y., & Li, P. (2024). A Crowd Behavior Analysis Method for Large-Scale Performances. In B. Sheng, L. Bi, J. Kim, N. Magnenat-Thalmann, & D. Thalmann (Eds.), Advances in Computer Graphics - 40th Computer Graphics International Conference, CGI 2023, Proceedings (pp. 54-66). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14498 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-50078-7_5

Zhang, Qian ; Huang, Tianyu ; Li, Yihao et al. / A Crowd Behavior Analysis Method for Large-Scale Performances. Advances in Computer Graphics - 40th Computer Graphics International Conference, CGI 2023, Proceedings. editor / Bin Sheng ; Lei Bi ; Jinman Kim ; Nadia Magnenat-Thalmann ; Daniel Thalmann. Springer Science and Business Media Deutschland GmbH, 2024. pp. 54-66 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{820e26206ca64a569aa98157489b32c2,

title = "A Crowd Behavior Analysis Method for Large-Scale Performances",

abstract = "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.",

keywords = "Crowd Behavior analysis, Crowd descriptors, Large-scale crowd performances",

author = "Qian Zhang and Tianyu Huang and Yihao Li and Peng Li",

note = "Publisher Copyright: {\textcopyright} 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 40th Computer Graphics International Conference, CGI 2023 ; Conference date: 28-08-2023 Through 01-09-2023",

year = "2024",

doi = "10.1007/978-3-031-50078-7_5",

language = "English",

isbn = "9783031500770",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "54--66",

editor = "Bin Sheng and Lei Bi and Jinman Kim and Nadia Magnenat-Thalmann and Daniel Thalmann",

booktitle = "Advances in Computer Graphics - 40th Computer Graphics International Conference, CGI 2023, Proceedings",

address = "Germany",

}

Zhang, Q, Huang, T, Li, Y & Li, P 2024, A Crowd Behavior Analysis Method for Large-Scale Performances. in B Sheng, L Bi, J Kim, N Magnenat-Thalmann & D Thalmann (eds), Advances in Computer Graphics - 40th Computer Graphics International Conference, CGI 2023, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14498 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 54-66, 40th Computer Graphics International Conference, CGI 2023, Shanghai, China, 28/08/23. https://doi.org/10.1007/978-3-031-50078-7_5

A Crowd Behavior Analysis Method for Large-Scale Performances. / Zhang, Qian; Huang, Tianyu; Li, Yihao et al.
Advances in Computer Graphics - 40th Computer Graphics International Conference, CGI 2023, Proceedings. ed. / Bin Sheng; Lei Bi; Jinman Kim; Nadia Magnenat-Thalmann; Daniel Thalmann. Springer Science and Business Media Deutschland GmbH, 2024. p. 54-66 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14498 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A Crowd Behavior Analysis Method for Large-Scale Performances

AU - Zhang, Qian

AU - Huang, Tianyu

AU - Li, Yihao

AU - Li, Peng

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 -

Zhang Q, Huang T, Li Y, Li P. A Crowd Behavior Analysis Method for Large-Scale Performances. In Sheng B, Bi L, Kim J, Magnenat-Thalmann N, Thalmann D, editors, Advances in Computer Graphics - 40th Computer Graphics International Conference, CGI 2023, Proceedings. Springer Science and Business Media Deutschland GmbH. 2024. p. 54-66. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-50078-7_5

A Crowd Behavior Analysis Method for Large-Scale Performances

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this