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Abstract

The present invention is embodied in a system and method for training a statistical model, such as a Bayesian network, to effectively capture probabilistic dependencies between the true state of an object being tracked and evidence from various tracking modalities to achieve robust digital vision tracking. The model can be trained and structured offline using data collected from sensors, that may be either vision or non-vision-based, in conjunction with position estimates from the sensing modalities. Both the individual reports about targets provided by visual processing modalities and inferences about the context-sensitive accuracies of the reports are considered. Dependencies among variables considered in the model can be restructured with Bayesian learning methods that revise the dependencies considered in the model. In use, the learned models for fusing multiple modalities of visual processing provide real-time position estimates by making inferences from reports from the modalities and by inferences about the context-specific reliabilities of one or more modalities.

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First Claim