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    Modeling dynamic swarms

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    Type
    Article
    Authors
    Ghanem, Bernard
    Ahuja, Narendra
    KAUST Department
    Visual Computing Center (VCC)
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Electrical Engineering Program
    VCC Analytics Research Group
    Date
    2013-01
    Permanent link to this record
    http://hdl.handle.net/10754/562560
    
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    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.
    Citation
    Ghanem, B., & Ahuja, N. (2013). Modeling dynamic swarms. Computer Vision and Image Understanding, 117(1), 1–11. doi:10.1016/j.cviu.2012.09.002
    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.
    Publisher
    Elsevier BV
    Journal
    Computer Vision and Image Understanding
    DOI
    10.1016/j.cviu.2012.09.002
    arXiv
    1102.1292
    Additional Links
    https://linkinghub.elsevier.com/retrieve/pii/S1077314212001282
    http://arxiv.org/pdf/1102.1292
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.cviu.2012.09.002
    Scopus Count
    Collections
    Articles; Electrical and Computer Engineering Program; Visual Computing Center (VCC); Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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