Robust visual tracking via multi-task sparse learning

@article{Zhang2012RobustVT,
  title={Robust visual tracking via multi-task sparse learning},
  author={Tianzhu Zhang and Bernard Ghanem and Si Liu and Narendra Ahuja},
  journal={2012 IEEE Conference on Computer Vision and Pattern Recognition},
  year={2012},
  pages={2042-2049}
}
In this paper, we formulate object tracking in a particle filter framework as a multi-task sparse learning problem, which we denote as Multi-Task Tracking (MTT). Since we model particles as linear combinations of dictionary templates that are updated dynamically, learning the representation of each particle is considered a single task in MTT. By employing popular sparsity-inducing ℓp, q mixed norms (p ∈ {2, ∞} and q = 1), we regularize the representation problem to enforce joint sparsity and… CONTINUE READING

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