Skip navigation
Skip navigation

Discriminative Multi-Task Sparse Learning for Robust Visual Tracking Using Conditional Random Field

Bozorgtabar, Behzad; Goecke, Roland


In this paper, we propose a discriminative multitask sparse learning scheme for object tracking in a particle filter framework. By representing each particle as a linear combination of adaptive dictionary templates, we utilise the correlations among different particles (tasks) to obtain a better representation and a more efficient scheme than learning each task individually. However, this model is completely generative and the designed tracker may not be robust enough to prevent the drifting...[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.2014.7008102


File Description SizeFormat Image
01_Bozorgtabar_Discriminative_Multi-Task_2014.pdf1.26 MBAdobe PDF    Request a copy

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

Updated:  19 May 2020/ Responsible Officer:  University Librarian/ Page Contact:  Library Systems & Web Coordinator