Visual Tracking and Dynamic Learning on the Grassmann Manifold with Inference from a Bayesian Framework and State Space Models
Paper i proceeding, 2011

We propose a novel visual tracking scheme that exploits both the geometrical structure of Grassmann manifold and piecewise geodesics under a Bayesian framework. Two particle filters are alternatingly employed on the manifold. One is used for online updating the appearance subspace on the manifold using sliding-window observations, and the other is for tracking moving objects on the manifold based on the dynamic shape and appearance models. Main contributions of the paper include: (a) proposing an online manifold learning strategy by a particle filter, where a mixture of dynamic models is used for both the changes of manifold bases in the tangent plane and the piecewise geodesics on the manifold. (b) proposing a manifold object tracker by incorporating object shape in the tangent plane and the manifold prediction error of object appearance jointly in a particle filter framework. Experiments performed on videos containing significant object pose changes show very robust tracking results. The proposed scheme also shows better performance as comparing with three existing trackers in terms of tracking drift and the tightness and accuracy of tracked boxes.

visual tracking

state space modeling

manifold tracking

particle filter

manifold learning

Grassmann manifold

piecewise geodesics


Zulfiqar Hasan Khan

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik

Irene Yu-Hua Gu

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik

Proceedings - International Conference on Image Processing, ICIP

15224880 (ISSN)

1433-1436 6115711
978-145771303-3 (ISBN)




Datorseende och robotik (autonoma system)


Informations- och kommunikationsteknik






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