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Visual tracking remains a challenging problem because of both intrinsic appearance variability of object and extrinsic disturbance. To deal with this problem, we present a novel approach for tracking based on the tensor features. We convert the image into tensor field to yield more discriminating features and encode the target appearance probabilistically with gaussian mixture model (GMM). The model...
This paper presents a novel formulation for object tracking. We model the second-order statistics of image regions and perform covariance matching under the variational level set framework. Specifically, covariance matrix is adopted as a visual object representation for partial differential equation (PDE) based contour tracking. Log-Euclidean calculus is used as a covariance distance metric instead...
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