Many approaches for effective object tracking have been proposed in the literature, among which the sparse representation has been a successful method for finding the best candidate with the minimal reconstruction error. We propose a generic object tracking algorithm using the sparse representation of the target's local patches. Sparse codes are adopted as a confidence measure to avoid drifting from the target during tracking. Experiments demonstrate that mentioned confidence measure can specify appearance change of the target accurately. Furthermore, given this measure, we propose a double search scheme for tracking targets. By using this approach, the proposed tracker can work with fewer particles than its rivals and as a result does not suffer from the extracomputation of redundant particles in a scene with no particular challenge. Moreover, a simple yet effective online template update approach is adopted in order to overcome the challenges such as abrupt illumination variation, the manifestation of occlusion, blurriness, or sudden movements of the target. Both quantitative and qualitative evaluations on a challenging dataset demonstrate a favorable performance compared to several state-of-the-art algorithms in terms of accuracy and robustness. (C) 2018 SPIE and IS&T
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