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]
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