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Adaptive Bayesian Object Tracking with Histograms of Dense Local Image Descriptors
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 Title & Authors
Adaptive Bayesian Object Tracking with Histograms of Dense Local Image Descriptors
Kim, Minyoung;
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 Abstract
Dense local image descriptors like SIFT are fruitful for capturing salient information about image, shown to be successful in various image-related tasks when formed in bag-of-words representation (i.e., histograms). In this paper we consider to utilize these dense local descriptors in the object tracking problem. A notable aspect of our tracker is that instead of adopting a point estimate for the target model, we account for uncertainty in data noise and model incompleteness by maintaining a distribution over plausible candidate models within the Bayesian framework. The target model is also updated adaptively by the principled Bayesian posterior inference, which admits a closed form within our Dirichlet prior modeling. With empirical evaluations on some video datasets, the proposed method is shown to yield more accurate tracking than baseline histogram-based trackers with the same types of features, often being superior to the appearance-based (visual) trackers.
 Keywords
Computer vision;Object tracking;Bayesian methods;Dense local image descriptors;
 Language
English
 Cited by
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