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.

Författare

Irene Yu-Hua Gu

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik

Zulfiqar Hasan Khan

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik

Publicerad i

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

s. 209-215
978-147992866-8 (ISBN)

Kategorisering

Styrkeområden

Informations- och kommunikationsteknik

Transport

Ämneskategorier (SSIF 2011)

Geometri

Systemvetenskap

Signalbehandling

Datorseende och robotik (autonoma system)

Identifikatorer

DOI

10.1109/spin.2014.6776949

ISBN

978-147992866-8

Mer information

Senast uppdaterat

2022-03-02