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In this paper, a novel subspace learning algorithm is proposed for robust visual tracking. Different from conventional sub-space based trackers, which first estimate the dimension of the subspace and then pursuit its basis to construct the subspace projection in appearance model, our method directly learns a low-rank projection with known ranks as subspace dimension to model the subspace structure...
Visual tracking is a fundamental task in computer vision. In this paper, we propose an incremental robust local dictionary learning framework to address this problem. We first initialize a dictionary using local low-rank features to represent the appearance subspace for the object. In this way, each candidate can be modeled by the sparse linear representation of the learnt dictionary. Then by incrementally...
This article develops a novel subspace learning algorithm for visual tracking. Specifically, the authors first present a linear projection view to formulate subspace learning and then develop a novel framework, called Latent Subspace Projection Pursuit (LSPP), to estimate the intrinsic dimension, removing corruptions and recovering the subspace structure for observed datasets. The authors evaluate...
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