Grassmann Manifold Online Learning and Partial Occlusion Handling for Visual Object Tracking under Bayesian Formulation
Paper i proceeding, 2012

This paper addresses issues of online learning and occlusion handling in video object tracking. Although manifold tracking is promising, large pose changes and long term partial occlusions of video objects remain challenging. We propose a novel manifold tracking scheme that tackles such problems, with the following main novelties: (a) Online estimation of object appearances on Grassmann manifolds; (b) Optimal criterion-based occlusion handling during online learning; (c) Nonlinear dynamic model for appearance basis matrix and its velocity; (b) Bayesian formulations separately for the tracking and the online learning process. Two particle filters are employed: one is on the manifold for generating appearance particles and another on the linear space for generating affine box particles. Tracking and online updating are performed in alternative fashion to mitigate the tracking drift. Experiments on videos have shown robust tracking performance especially when objects contain significant pose changes accompanied with long-term partial occlusions. Evaluations and comparisons with two existing methods provide further support to the proposed method.

Grassmann manifolds

online learning

state space models

visual object tracking

nonlinear models

occlusion handling

Bayesian tracking


Irene Yu-Hua Gu

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik

Zulfiqar Hasan Khan

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik

Proceedings - International Conference on Pattern Recognition

10514651 (ISSN)

978-499064410-9 (ISBN)


Informations- och kommunikationsteknik



Data- och informationsvetenskap




Sannolikhetsteori och statistik

Datavetenskap (datalogi)

Datorseende och robotik (autonoma system)



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