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The attached document may provide the author's accepted version of a published work. See Citation for details of the published work. A Dynamic Model for Occlusion Adaptive Joint Tracking Content Areas: probabilistic reasoning, vision, model-based reasoning This paper proposes a dynamic model supporting multimodal state space probability distributions and presents the application of the model in dealing with visual occlusions when tracking multiple objects jointly. For a set of occlusion relationship hypotheses, a frame is measured once under each hypothesis, and a set of measurements is obtained at each time instant. Both the occlusion relationship and state of the objects are recursively estimated from the history of measurement data. Two computationally efficient filtering algorithms are derived for online joint tracking.