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This paper presents a novel tracking algorithm based on the convex hull representation model with sparse representation. The tracked object is assumed to be within the object convex hull and the candidate convex hull in the meanwhile. The object convex hull consists of a principle component analysis (PCA) subspace, and the candidate convex hull is constructed by all candidate samples with the sparsity...
This paper proposes a novel framework to alleviate the model drift problem in visual tracking, which is based on paced updates and trajectory selection. Given a base tracker, an ensemble of trackers is generated, in which each tracker's update behavior will be paced and then traces the target object forward and backward to generate a pair of trajectories in an interval. Then, we implicitly perform...
In this paper, we propose a generative tracking method based on a novel robust linear regression algorithm. In contrast to existing methods, the proposed Least Soft-thresold Squares (LSS) algorithm models the error term with the Gaussian-Laplacian distribution, which can be solved efficiently. Based on maximum joint likelihood of parameters, we derive a LSS distance to measure the difference between...
Fragment-based tracking methods have shown its robustness in handling partial occlusion and pose change. In this paper, we propose a novel fragment-based tracking approach using on online multiple kernel learning (MKL) method. An online MKL method for object tracking is implemented by considering temporal continuity explicitly. Instead of directly using multiple features of objects, we employ MKL...
In this paper we present an effective and fast tracking algorithm, in which object tracking is achieved by solving L2-regularized least square (L2-RLS) problem-s within a Bayesian inference framework. Firstly, we model the appearance of the tracked target with P-CA basis vectors and square templates which make the tracker not only exploit the strength of sub space repre-senation but also explicitly...
In this paper, we propose a novel tracking framework, multi-cues spatial pyramid matching (MSPM). Different cues are used to generate a set of probability maps, where the value of each pixel indicates the probability that it belongs to the foreground. Then those probability maps are combined into a single probability map by a weighted linear function. There exist two main contributions. First, a generic...
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