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2000

Resolving Visual Uncertainty and Occlusion through Probabilistic Reasoning

14 years 1 months ago
Resolving Visual Uncertainty and Occlusion through Probabilistic Reasoning
Tracking interacting human body parts from a single two-dimensional view is difficult due to occlusion, ambiguity and spatio-temporal discontinuities. We present a Bayesian network method for this task. The method is not reliant upon spatio-temporal continuity, but exploits it when present. Our inferencebased tracking model is compared with a CONDENSATION model augmented with a probabilistic exclusion mechanism. We show that the Bayesian network has the advantages of fully modelling the state space, explicitly representing domain knowledge, and handling complex interactions between variables in a globally consistent and computationally effective manner.
Jamie Sherrah, Shaogang Gong
Added 01 Nov 2010
Updated 01 Nov 2010
Type Conference
Year 2000
Where BMVC
Authors Jamie Sherrah, Shaogang Gong
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