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.

Hybrid Joint-Separable Multibody Tracking

Lanz, Oswald;
2005

Abstract

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.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11582/11308
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