Oumaima Sliti
ABSTRACT
Sparse representation is widely used in visual tracking thanks to its efficiency and ability to handle appearance changes. In this paper, we propose to improve the original mean shift tracking algorithm, by defining its target model and candidates with a sparse approximation. The Method of Optimal Directions MOD is employed to learn an over-complete dictionary, afterward the Orthogonal Matching Pursuit OMP is used to present the appearance model with the potential atoms of the dictionary. We project the generated vector into the successive frames to detect the target region. Thus, the exploitation of the spatial information is demonstrated by the process of back-projecting the signature vector template in each frame. Our tracker attempts to perfectly localize random objects in different scenarios, and proved to be robust against different challenges. In fact, the proposed approach guarantees a total separation between the target and its background. Our tracker proved to be more stable and less prone to drift away.
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Index Terms
- Method of optimal directions for visual tracking
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