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- W2364487724 abstract "To cope with appearance variations of the target object during visual tracking,a nonparametric Bayesian multi-modal appearance model for learning over time was proposed.First,by taking the temporal Dirichlet process as prior distribution,the proposed model separated target samples previously estimated into several clusters.Each cluster represented a certain type of the target appearance,which was modeled as discriminative classifiers.Then,to balance the trade-off between the classification error of appearance model and the cost for splitting the clusters,the multi-modal appearance model was automatically learned by the use of Bayesian posterior inference.Finally,based on the Noisy-OR model,a greedy algorithm was used to discriminate the target object by combining the outputs of appearance classifiers.The simulation results show that the proposed method can track an object under rapid appearance changes and achieve better tracking results with high accuracy." @default.
- W2364487724 created "2016-06-24" @default.
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- W2364487724 date "2013-01-01" @default.
- W2364487724 modified "2023-09-24" @default.
- W2364487724 title "On-line Target Tracking Using Temporal Dirichlet Process Mixture Model" @default.
- W2364487724 hasPublicationYear "2013" @default.
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