Hybrid Joint-Separable Multibody Tracking

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Hybrid Joint-Separable Multibody Tracking
Statistical models for tracking different moving bodies must be able to reason about occlusions in order to be effective. Representing the joint statistics across different bodies is computationally hard, since the size of the representation grows exponentially with the number of bodies being tracked. Separable tracking, with one tracker per body, cannot deal with occlusions effectively. We propose a new model, dubbed Hybrid Joint-Separable (HJS), that uses a representation size that grows linearly with the number of bodies, and a computational complexity that grows quadratically. This model can reason explicitly about occlusions. We describe a particle filter implementation of this model, and present promising experimental results.
Oswald Lanz, Roberto Manduchi
Added 12 Oct 2009
Updated 29 Oct 2009
Type Conference
Year 2005
Where CVPR
Authors Oswald Lanz, Roberto Manduchi
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