crowd simulation
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2022 ◽  
Vol 32 (1) ◽  
pp. 1-33
Author(s):  
Jinghui Zhong ◽  
Dongrui Li ◽  
Zhixing Huang ◽  
Chengyu Lu ◽  
Wentong Cai

Data-driven crowd modeling has now become a popular and effective approach for generating realistic crowd simulation and has been applied to a range of applications, such as anomaly detection and game design. In the past decades, a number of data-driven crowd modeling techniques have been proposed, providing many options for people to generate virtual crowd simulation. This article provides a comprehensive survey of these state-of-the-art data-driven modeling techniques. We first describe the commonly used datasets for crowd modeling. Then, we categorize and discuss the state-of-the-art data-driven crowd modeling methods. After that, data-driven crowd model validation techniques are discussed. Finally, six promising future research topics of data-driven crowd modeling are discussed.


2021 ◽  
Author(s):  
Michelangelo Diamanti ◽  
Hannes Hogni Vilhjalmsson
Keyword(s):  

2021 ◽  
Vol 33 (9) ◽  
pp. 1337-1348
Author(s):  
Pei Lyu ◽  
Weichao Chen ◽  
Quan Zhang ◽  
Mingliang Xu ◽  
Long Huang ◽  
...  

Author(s):  
Soraia Raupp Musse ◽  
Vinicius Jurinic Cassol ◽  
Daniel Thalmann
Keyword(s):  
The Past ◽  

Author(s):  
Douglas Ivo ◽  
Joaquim Cavalcante-Neto ◽  
Creto Vidal

Author(s):  
Robert Clay ◽  
Jonathan A. Ward ◽  
Patricia Ternes ◽  
Le-Minh Kieu ◽  
Nick Malleson

Author(s):  
Wouter van Toll ◽  
Thomas Chatagnon ◽  
Cédric Braga ◽  
Barbara Solenthaler ◽  
Julien Pettré

2021 ◽  
Vol 40 (2) ◽  
pp. 731-754
Author(s):  
W. Toll ◽  
J. Pettré
Keyword(s):  

Author(s):  
Sainan Zhang ◽  
Jun Zhang ◽  
Mohcine Chraibi ◽  
Weiguo Song
Keyword(s):  

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