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• W933163513 abstract "A probabilistic real time tracking algorithm is proposed where the target’s feature distribution is represented by a Gaussian mixture model (GMM). The target localization is achieved by maximizing its weighted likelihood in the image sequence. The role of the weight in the likelihood definition is important as it allows gradient based optimization to be performed, which would not be feasible in a context of standard likelihood representations. Moreover, the algorithm handles scale and rotation changes of the target, as well as appearance changes, which modifies the components of the GMM. The real time performance is experimentally confirmed, while the algorithms has comparative performance with other state-of-the-art tracking algorithms." @default.
• W933163513 created "2016-06-24" @default.
• W933163513 creator A5005367675 @default.
• W933163513 creator A5007939716 @default.
• W933163513 creator A5058318737 @default.
• W933163513 date "2015-11-01" @default.
• W933163513 modified "2023-09-27" @default.
• W933163513 title "Visual tracking using spatially weighted likelihood of Gaussian mixtures" @default.
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• W933163513 doi "https://doi.org/10.1016/j.cviu.2015.07.003" @default.
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