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Secondly, an MFH based pairwise appearance model is designed.
In particular, we propose to enhance the discriminative ability of the appearance model in three-fold.
Firstly, an SRC based global discriminative appearance model is designed for discriminating all targets.
The cooperative appearance model uses local collaborative representation to rectify holistic representation with impact regions.
In this paper, an object-tracking algorithm via a cooperative appearance model is proposed.
Conventional algorithms design local and holistic appearance model for effectively tracking.
By this way, the global discriminative appearance model can distinguish different targets more effectively.
In our tracker, candidate targets are linearly combined by using the structural local sparse appearance model.
In this paper, by using pairwise metric learning, we present a novel appearance model for robust visual tracking.
To effectively handle the deformable targets, a target is first represented by a local patch-based appearance model.
In order to fulfill the requirements of tracking robustness and effectiveness in practical conditions, a dynamic appearance model is constructed.
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