Sparse Representation Tracking with Auxiliary Adaptive Appearance Models

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We propose an effective tracking algorithm based on sparse representation and auxiliary adaptive appearance modeling. Based on a sparse representation, l1 minimization can follow targets in challenging situations. Unfortunately, tracking approaches based on l1 minimization are likely to be inefficient because they measure using dense coefficient distributions. The number of target candidates can be very large when the state space is densely sampled. Each minimization takes long time to find the solution. Traditionally, we must calculate the coefficients for each tracking candidates, which is computationally expensive. In this work, we found that l1 minimization can be limited to a few regions with the reasonable probability based on adaptive appearance modeling and background probability estimation. Therefore, the computational cost is greatly reduced. We have also found that appearance information is useful for the region selection. We form the basis of appearance modeling using colors and shapes. The results of the experiment show that the proposed tracker has good performance.

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