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The classical mean-shift tracking algorithm is based on histogram of colors, which is vulnerable to light change. In order to overcome the drawback, we presents a new metric used for tracking. Firstly, we compute the curvature property of an image and choose scale through maximizing the second derivative in horizontal direction of points in the inner elliptical region on the target. Then we compute...
This paper presents an adaptive tracking algorithm by online features enhancement. To avoid the distraction of the similar background on tracker, Bayes decision rule is applied to calculate the posterior probability of every pixel belonging to the object and generate a set of candidate confidence maps according to the conditional sample densities from object and background on different features. We...
The modeling of the object appearance is one of the key issues in the development and application of effective object tracking. This paper presents a tracking algorithm based on representing the appearance of the object using a sparse representation based subspace model. the sparse representation theory offers us a powerful tool to model the object by only a small fraction of the training set. The...
One of the key issues related to object tracking is the representation of the object motion. It is a challenging problem because the object usually exhibits complex and rich dynamic behavior. In this paper, we propose an adaptive dynamic model to describe the dynamics/motion of the object and embed it into the particle filter framework for visual object tracking. The model characterize the object...
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