Skip navigation
Skip navigation

Robust Visual Tracking via Rank-Constrained Sparse Learning

Bozorgtabar, Behzad; Goecke, Roland


In this paper, we present an improved low-rank sparse learning method for particle filter based visual tracking, which we denote as rank-constrained sparse learning. Since each particle can be sparsely represented by a linear combination of the bases from an adaptive dictionary, we exploit the underlying structure between particles by constraining the rank of particle sparse representations jointly over the adaptive dictionary. Besides utilising a common structure among particles, the proposed...[Show more]

CollectionsANU Research Publications
Date published: 2014
Type: Conference paper
Source: Reflective Features Detection and Hierarchical Reflections Separation in Image Sequences
DOI: 10.1109/DICTA.2014.7008129


File Description SizeFormat Image
01_Bozorgtabar_Robust_Visual_Tracking_via_2014.pdf483.45 kBAdobe PDF    Request a copy

Items in Open Research are protected by copyright, with all rights reserved, unless otherwise indicated.

Updated:  12 November 2018/ Responsible Officer:  University Librarian/ Page Contact:  Library Systems & Web Coordinator