Robust Visual Tracking via Structured Multi-Task Sparse Learning
International Journal of Computer Vision
Sparse coding based visual tracking: Review and experimental comparison
Pattern Recognition
A survey of appearance models in visual object tracking
ACM Transactions on Intelligent Systems and Technology (TIST) - Survey papers, special sections on the semantic adaptive social web, intelligent systems for health informatics, regular papers
A novel particle filter with implicit dynamic model for irregular motion tracking
Machine Vision and Applications
Object tracking using learned feature manifolds
Computer Vision and Image Understanding
Collaborative object tracking model with local sparse representation
Journal of Visual Communication and Image Representation
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Recently sparse representation has been applied to visual tracker by modeling the target appearance using a sparse approximation over a template set, which leads to the so-called L1 trackers as it needs to solve an ℓ1 norm related minimization problem for many times. While these L1 trackers showed impressive tracking accuracies, they are very computationally demanding and the speed bottleneck is the solver to ℓ1 norm minimizations. This paper aims at developing an L1 tracker that not only runs in real time but also enjoys better robustness than other L1 trackers. In our proposed L1 tracker, a new ℓ1 norm related minimization model is proposed to improve the tracking accuracy by adding an ℓ1 norm regularization on the coefficients associated with the trivial templates. Moreover, based on the accelerated proximal gradient approach, a very fast numerical solver is developed to solve the resulting ℓ1 norm related minimization problem with guaranteed quadratic convergence. The great running time efficiency and tracking accuracy of the proposed tracker is validated with a comprehensive evaluation involving eight challenging sequences and five alternative state-of-the-art trackers.