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We study the use of optical flow as a characteristic for tracking. We analyze the behavior of three flow-based observation models for particle filter algorithms, and compare the results with those obtained using a well-known, gradient-based, observation model. Although in theory, optical flow could be used directly to displace an object model, in practice, flow estimation techniques lack the necessary precision. In view of the fact that probabilistic tracking algorithms enable imprecise or incomplete information to be handled naturally, these models have been used as a natural way of incorporating flow information into the tracking.