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 tracke...

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Bibliographic Details
Published in2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) Vol. 1; pp. 413 - 420 vol. 1
Main Authors Lanz, O., Manduchi, R.
Format Conference Proceeding
LanguageEnglish
Published IEEE 2005
Subjects
Computational complexity
Computational efficiency
Cultural differences
Filtering theory
Layout
Particle filters
Particle tracking
Shape measurement
Statistics
Target tracking
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Summary: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.
ISBN:0769523722
9780769523729
ISSN:1063-6919
1063-6919
DOI:10.1109/CVPR.2005.178