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Bayesian Online Learning on the Riemannian Manifold using A Dual Model with Applications to Video Object Tracking

Khan, Zulfiqar Hasan ; Gu, Irene Yu-Hua

2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops). IEEE International Conference on Computer Vision (ICCV), Barcelona, 6-13 November 2011, pp.1402-1409

2011

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  • Título:
    Bayesian Online Learning on the Riemannian Manifold using A Dual Model with Applications to Video Object Tracking
  • Autor: Khan, Zulfiqar Hasan ; Gu, Irene Yu-Hua
  • Assuntos: Datorteknik ; Computer Engineering ; Signalbehandling ; Signal Processing ; Datorseende Och Robotik (Autonoma System) ; Computer Vision And Robotics (Autonomous Systems) ; Riemannian Manifold ; Dual Model ; Visual Object Tracking ; Online Learning
  • É parte de: 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops). IEEE International Conference on Computer Vision (ICCV), Barcelona, 6-13 November 2011, pp.1402-1409
  • Descrição: This paper proposes a new Bayesian framework-based online learning method on a Riemannian manifold for video objects. The basic idea is to consider the dynamic appearance of an object as a point moving on a nonlinear smoothing surface (Riemannian manifold), where a dual model is applied for estimating the posterior trajectory of this moving point at each time instant under the Bayesian framework. The key difference of our method is to use a set of particle manifold points generated from the same time instant for computing the Riemannian mean, rather than using a sliding window of manifold points at different times for computing a Riemannian mean in most existing Riemannian manifold tracking methods. The dual model uses two state variables for modeling the online learning process on Riemannian manifolds: one is for object appearances on Riemannian manifolds, another is for velocity vectors in tangent planes of manifolds. A particle filter is employedon the manifold to generate the posterior...
  • Data de publicação: 2011
  • Idioma: Inglês

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