CPL - Chalmers Publication Library
| Utbildning | Forskning | Styrkeområden | Om Chalmers | In English In English Ej inloggad.

Domain-Shift Tracking: Online Learning and Visual Object Tracking on Smooth Manifolds

Irene Y.H. Gu (Institutionen för signaler och system, Signalbehandling) ; Zulfiqar H. Khan (Institutionen för signaler och system, Signalbehandling)
Proceedings of the 1st International Conference on Signal Processing and Integrated Networks, SPIN 2014; Noida; India; 20 February 2014 through 21 February 2014 p. 209-215. (2014)
[Konferensbidrag, refereegranskat]

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.

Nyckelord: object tracking, domain-shift tracking, domainshift online learning, manifold tracking, Grassmann manifolds, Riemannian manifolds, particle filters, Bayesian tracking, piecewise geodesic, nonlinear state space model.

Article number 6776949

Den här publikationen ingår i följande styrkeområden:

Läs mer om Chalmers styrkeområden  

Denna post skapades 2014-05-14. Senast ändrad 2015-04-07.
CPL Pubid: 198045