Domain-Shift Tracking: Online Learning and Visual Object Tracking on Smooth Manifolds
Paper i proceeding, 2014

This paper describes a novel domain-shift tracking scheme that includes Bayesian formulation on the Grassmann/ Riemannian manifold for tracking, and domain-shift online object learning as well as occlusion handling on the manifold. Since out-of-plane object images do not lie in a single vector space, smoothing manifolds are more suitable tools for describing domain-shift nature of such dynamic object images. The proposed domain-shift scheme is designed for tracking large-size dynamic objects (i.e. camera is close to the object) in video that contain significant out-of-plane pose changes, and may be accompanied with long-term partial occlusions. The main features of such domain-shift tracker include: (a) Bayesian formulation defined on a manifold instead of vector space, performing posterior state estimation on the manifold based on nonlinear state space modeling; (b) Two particle filters defined on the manifold, one for online learning, another for tracking; (c) Occlusion handling is added to the online learning process to prevent learning occluding objects/clutter. To show the variant of domain-shift trackers, two example schemes are described: one uses instantaneous data on Riemannian manifolds, another uses a sliding-window of data on Grassmann manifolds. Tests on videos from the proposed domain shift trackers have shown very robust tracking performance when large-size objects contain significant out-of-plane pose changes accompanied with long-term partial occlusions. Comparisons with three existing state-of-the-art methods provide further support to the proposed scheme.

particle filters

Bayesian tracking

manifold tracking

domainshift online learning

Grassmann manifolds

object tracking

Riemannian manifolds

piecewise geodesic

domain-shift tracking

nonlinear state space model.


Irene Yu-Hua Gu

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik

Zulfiqar Hasan Khan

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik

Proceedings of the 1st International Conference on Signal Processing and Integrated Networks, SPIN 2014; Noida; India; 20 February 2014 through 21 February 2014

978-147992866-8 (ISBN)


Informations- och kommunikationsteknik






Datorseende och robotik (autonoma system)





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