Abstract
Visual object tracking plays an essential role in vision based applications. Most of the previous research has limitations due to the non-discriminated features used or the focus on simple template matching without the consideration of appearance variations. To address these challenges, this paper proposes a new approach for robust visual object tracking via sparse representation and reconstruction, where two main contributions are devoted in terms of object representation and location respectively. And the sparse representation and reconstruction (SR2) are integrated into a Kalman filter framework to form a robust object tracker named as SR2KF tracker. The extensive experiments show that the proposed tracker is able to tolerate the appearance variations, background clutter and image deterioration, and outperforms the existing work.
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Han, Z., Ye, Q., Jiao, J. (2013). Robust Visual Object Tracking via Sparse Representation and Reconstruction. In: Wilson, R., Hancock, E., Bors, A., Smith, W. (eds) Computer Analysis of Images and Patterns. CAIP 2013. Lecture Notes in Computer Science, vol 8048. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40246-3_35
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DOI: https://doi.org/10.1007/978-3-642-40246-3_35
Publisher Name: Springer, Berlin, Heidelberg
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