Abstract
We present an algorithm which tracks multiple objects for video surveillance applications. This algorithm is based on a Bayesian framework and a Particle filter. In order to use this method in practical applications we define a statistical model of the object appearance to build a robust likelihood function. The tracking process is only based on image data, therefore, a previous step to learn the object shape and their motion parameters is not necessary. Using the localization results, we can define a prior density which is used to initialize the algorithm. Finally, our method has been proved successfully in several sequences and its performance is more accurate than classical filters.
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© 2003 Springer-Verlag Berlin Heidelberg
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Varona, J., Gonzàlez, J., Roca, F.X., Villanueva, J.J. (2003). Appearance Tracking for Video Surveillance. 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_120
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DOI: https://doi.org/10.1007/978-3-540-44871-6_120
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