Abstract
In this paper, we present two new observation models based on optical flow information to track objects using particle filter algorithms. Although optical flow information enables us to know the displacement of objects present in a scene, it cannot be used directly to displace an object model since flow calculation 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 means of incorporating flow information into the tracking.
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Lucena, M.J., Fuertes, J.M., de la Blanca, N.P., Garrido, A., Ruiz, N. (2003). Probabilistic Observation Models for Tracking Based on Optical Flow. In: Perales, F.J., Campilho, A.J.C., de la Blanca, N.P., Sanfeliu, A. (eds) Pattern Recognition and Image Analysis. IbPRIA 2003. Lecture Notes in Computer Science, vol 2652. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-44871-6_54
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DOI: https://doi.org/10.1007/978-3-540-44871-6_54
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