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- W2896526929 abstract "Currently1, Compressive Tracking (CT) method has been widely concerned for its high efficiency. However, when it comes to occlusion and scale variation, it may make the objects shifting and lost. In order to handle this problem, we propose an improved compressive tracking method based on super-pixel structured information. Firstly, constructing super-pixel chromaticity confidence maps to estimate the coarse position of the target. Simultaneously, utilizing Euclidean distance between cluster centers of different super-pixel patches to calculate scale ratio to obtain target scale of the current frame. Secondly, a special Measurement Matrix combined super-pixel structured information is constructed to lower the Haar-like feature dimension. After that, the Native Bayes Classifier is performed. The target location is finally estimated accurately. Extensive experiments on some open video sequences on OTB-2013 demonstrate that our approach can not only adapt to scale transform, but also improve the accuracy in the case of real-time tracking." @default.
- W2896526929 created "2018-10-26" @default.
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- W2896526929 date "2018-10-22" @default.
- W2896526929 modified "2023-10-16" @default.
- W2896526929 title "Compressive Tracking Based on Super-pixel Structured Information" @default.
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- W2896526929 doi "https://doi.org/10.1145/3207677.3277927" @default.
- W2896526929 hasPublicationYear "2018" @default.
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