Modeling dynamic swarms

Handle URI:
http://hdl.handle.net/10754/562560
Title:
Modeling dynamic swarms
Authors:
Ghanem, Bernard ( 0000-0002-5534-587X ) ; Ahuja, Narendra
Abstract:
This paper proposes the problem of modeling video sequences of dynamic swarms (DSs). We define a DS as a large layout of stochastically repetitive spatial configurations of dynamic objects (swarm elements) whose motions exhibit local spatiotemporal interdependency and stationarity, i.e., the motions are similar in any small spatiotemporal neighborhood. Examples of DS abound in nature, e.g., herds of animals and flocks of birds. To capture the local spatiotemporal properties of the DS, we present a probabilistic model that learns both the spatial layout of swarm elements (based on low-level image segmentation) and their joint dynamics that are modeled as linear transformations. To this end, a spatiotemporal neighborhood is associated with each swarm element, in which local stationarity is enforced both spatially and temporally. We assume that the prior on the swarm dynamics is distributed according to an MRF in both space and time. Embedding this model in a MAP framework, we iterate between learning the spatial layout of the swarm and its dynamics. We learn the swarm transformations using ICM, which iterates between estimating these transformations and updating their distribution in the spatiotemporal neighborhoods. We demonstrate the validity of our method by conducting experiments on real and synthetic video sequences. Real sequences of birds, geese, robot swarms, and pedestrians evaluate the applicability of our model to real world data. © 2012 Elsevier Inc. All rights reserved.
KAUST Department:
Visual Computing Center (VCC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Electrical Engineering Program; VCC Analytics Research Group
Publisher:
Elsevier
Journal:
Computer Vision and Image Understanding
Issue Date:
Jan-2013
DOI:
10.1016/j.cviu.2012.09.002
Type:
Article
ISSN:
10773142
Sponsors:
The support of the Office of Naval Research under Grant N00014-09-1-0017 and the National Science Foundation under grant IIS 08-12188 is gratefully acknowledged.
Appears in Collections:
Articles; Electrical Engineering Program; Visual Computing Center (VCC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorGhanem, Bernarden
dc.contributor.authorAhuja, Narendraen
dc.date.accessioned2015-08-03T10:42:44Zen
dc.date.available2015-08-03T10:42:44Zen
dc.date.issued2013-01en
dc.identifier.issn10773142en
dc.identifier.doi10.1016/j.cviu.2012.09.002en
dc.identifier.urihttp://hdl.handle.net/10754/562560en
dc.description.abstractThis paper proposes the problem of modeling video sequences of dynamic swarms (DSs). We define a DS as a large layout of stochastically repetitive spatial configurations of dynamic objects (swarm elements) whose motions exhibit local spatiotemporal interdependency and stationarity, i.e., the motions are similar in any small spatiotemporal neighborhood. Examples of DS abound in nature, e.g., herds of animals and flocks of birds. To capture the local spatiotemporal properties of the DS, we present a probabilistic model that learns both the spatial layout of swarm elements (based on low-level image segmentation) and their joint dynamics that are modeled as linear transformations. To this end, a spatiotemporal neighborhood is associated with each swarm element, in which local stationarity is enforced both spatially and temporally. We assume that the prior on the swarm dynamics is distributed according to an MRF in both space and time. Embedding this model in a MAP framework, we iterate between learning the spatial layout of the swarm and its dynamics. We learn the swarm transformations using ICM, which iterates between estimating these transformations and updating their distribution in the spatiotemporal neighborhoods. We demonstrate the validity of our method by conducting experiments on real and synthetic video sequences. Real sequences of birds, geese, robot swarms, and pedestrians evaluate the applicability of our model to real world data. © 2012 Elsevier Inc. All rights reserved.en
dc.description.sponsorshipThe support of the Office of Naval Research under Grant N00014-09-1-0017 and the National Science Foundation under grant IIS 08-12188 is gratefully acknowledged.en
dc.publisherElsevieren
dc.subjectComputer visionen
dc.subjectCrowd behavior analysisen
dc.subjectDynamic texturesen
dc.subjectSpatiotemporal analysisen
dc.subjectSwarmsen
dc.titleModeling dynamic swarmsen
dc.typeArticleen
dc.contributor.departmentVisual Computing Center (VCC)en
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentElectrical Engineering Programen
dc.contributor.departmentVCC Analytics Research Groupen
dc.identifier.journalComputer Vision and Image Understandingen
dc.contributor.institutionUniversity of Illinois at Urbana-Champaign, Electrical and Computer Engineering Department, United Statesen
kaust.authorGhanem, Bernarden
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