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- W2794366265 abstract "Designing a discriminative speed function plays a vital role in conducting contour evolution in level set-based tracking framework. In this work, we propose a superpixel-driven speed function modeling method by fusion of two supplementary cues: appearance and motion. Based on kernel density estimation, a discriminative model separating object from background is constructed in appearance channel. Meanwhile, by making use of the statistical characteristics of the optical flow field, the relative motion between object and background can be distinguished and enhanced by an adaptively chosen threshold. Finally, we combine these two cues in a decision level to propose a speed function with more discriminative power under the Semi-Naive Bayes framework. Experimental results on a number of challenging video sequences demonstrate the effectiveness and robustness of the proposed tracking methods." @default.
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- W2794366265 date "2017-12-01" @default.
- W2794366265 modified "2023-09-24" @default.
- W2794366265 title "Superpixel-based level set tracking by integrating appearance and motion" @default.
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- W2794366265 doi "https://doi.org/10.1109/vcip.2017.8305059" @default.
- W2794366265 hasPublicationYear "2017" @default.
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