Gradient-based steering for vision-based crowd simulation algorithms

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• dc.contributor.author Dutra, T. B.ca
• dc.contributor.author Marques, Ricardoca
• dc.contributor.author Cavalcante‐Neto, J.B.ca
• dc.contributor.author Vidal, C. A.ca
• dc.contributor.author Pettré, J.ca
• dc.date.accessioned 2018-10-02T16:09:15Z
• dc.date.available 2018-10-02T16:09:15Z
• dc.date.issued 2017
• dc.description.abstract Most recent crowd simulation algorithms equip agents with a synthetic vision component for steering. They offer promising perspectives through a more realistic simulation of the way humans navigate according to their perception of the surrounding environment. In this paper, we propose a new perception/motion loop to steering agents along collision free trajectories that significantly improves the quality of vision-based crowd simulators. In contrast with solutions where agents avoid collisions in a purely reactive (binary) way, we suggest exploring the full range of possible adaptations and retaining the locally optimal one. To this end, we introduce a cost function, based on perceptual variables, which estimates an agent’s situation considering both the risks of future collision and a desired destination. We then compute the partial derivatives of that function with respect to all possible motion adaptations. The agent then adapts its motion by following the gradient. This paper has thus two main contributions: the definition of a general purpose control scheme for steering synthetic vision-based agents; and the proposition of cost functions for evaluating the perceived danger of the current situation. We demonstrate improvements in several cases.en
• dc.description.sponsorship T. B. Dutra acknowl1edges CAPES for the fellowship (PDSE Proc. 0130/13-3) and J. Ondˇrej for helping in the implementation of his model. R. Marques acknowledges the projects Percolation (ANR- 13-JS02-0008), and Kristina (H2020-RIA-645012) for partially financing this research.en
• dc.format.mimetype application/pdf
• dc.identifier.citation Dutra TB, Marques R, Cavalcante-Neto JB, Vidal CA, Pettré J. Gradient-based steering for vision-based crowd simulation algorithms. Comput Graph Forum. 2017;36:337-48. DOI: 10.1111/cgf.13130
• dc.identifier.doi http://dx.doi.org/10.1111/cgf.13130
• dc.identifier.issn 0167-7055
• dc.identifier.uri http://hdl.handle.net/10230/35550
• dc.language.iso eng
• dc.publisher Wileyca
• dc.relation.ispartof Computer graphics forum : journal of the European Association for Computer Graphics. 2017;36:337-48.
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/645012
• dc.rights This is the peer reviewed version of the following article: Dutra TB, Marques R, Cavalcante-Neto JB, Vidal CA, Pettré J. Gradient-based steering for vision-based crowd simulation algorithms. Comput Graph Forum. 2017;36:337-48, which has been published in final form at http://dx.doi.org/10.1111/cgf.13130 This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.subject.keyword Three-dimensional graphics and realismen
• dc.subject.keyword Types of simulationen
• dc.subject.keyword Animationen
• dc.title Gradient-based steering for vision-based crowd simulation algorithmsca
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/acceptedVersion