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

Bozorgtabar, Behzad; Goecke, Roland

Description

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
URI: http://hdl.handle.net/1885/67217
Source: Reflective Features Detection and Hierarchical Reflections Separation in Image Sequences
DOI: 10.1109/DICTA.2014.7008102

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