Modeling small group behaviors in large crowd simulation

Park, Seung In; Cao, Yong; Quek, Francis

doi:10.1145/2159616.2159659

Cited by 1 publication

(2 citation statements)

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“…In contrast, macroscopic approaches regard a crowd as continuous flows and focus on modelling the dynamics of the entire crowd, for example continuum dynamics [TCP06, JXM*10], aggregate dynamics [NGCL09] and navigation field [PvdBC*11, TWCL18]. There are also hybrid models that extend the continuum dynamics from an agent based perspective [PCQ12]. These macroscopic approaches are hybrid methods that enforce local interactions separately from the macroscopic algorithm, which affects their generalizability.…”

Section: Related Workmentioning

confidence: 99%

“…Empirical methods abstract observed crowd behaviours into explicit mathematical models and deterministic systems. They can model various crowd behaviours at different levels of granularity, for example, macroscopic pedestrian flows [TCP06, NGCL09], microscopic local interactions [Rey87, HM95, KSG14], and mesoscopic combinations of local behaviours and global navigation [PCQ12]. We refer to these methods as model‐based , since they are explicitly formulated by observed human behaviours using explicable mathematical terms.…”

Section: Introductionmentioning

confidence: 99%

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Model‐based Crowd Behaviours in Human‐solution Space

Wei

Wang

Zhang

et al. 2023

Computer Graphics Forum

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Realistic crowd simulation has been pursued for decades, but it still necessitates tedious human labour and a lot of trial and error. The majority of currently used crowd modelling is either empirical (model‐based) or data‐driven (model‐free). Model‐based methods cannot fit observed data precisely, whereas model‐free methods are limited by the availability/quality of data and are uninterpretable. In this paper, we aim at taking advantage of both model‐based and data‐driven approaches. In order to accomplish this, we propose a new simulation framework built on a physics‐based model that is designed to be data‐friendly. Both the general prior knowledge about crowds encoded by the physics‐based model and the specific real‐world crowd data at hand jointly influence the system dynamics. With a multi‐granularity physics‐based model, the framework combines microscopic and macroscopic motion control. Each simulation step is formulated as an energy optimization problem, where the minimizer is the desired crowd behaviour. In contrast to traditional optimization‐based methods which seek the theoretical minimizer, we designed an acceleration‐aware data‐driven scheme to compute the minimizer from real‐world data in order to achieve higher realism by parameterizing both velocity and acceleration. Experiments demonstrate that our method can produce crowd animations that are more realistically behaved in a variety of scales and scenarios when compared to the earlier methods.

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