A Bayesian Occlusion Model for Sequential Object Matching

We consider the problem of locating instances of a known object in a novel scene by matching the fiducial features of the object. Our approach to the problem consists of two parts: a model for the appearance of the features and a model for the shape of the object. We then bind these parts together in a Bayesian framework and match the features sequentially, using the information about the locations of previously matched features. Into this matching system we add a Bayesian model for dealing with features that are not detected due to occlusion or abnormal appearance. Our system yields promising results, losing little matching accuracy even for heavily occluded objects.

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