Sparse coding based visual tracking: Review and experimental comparison

Recently, sparse coding has been successfully applied in visual tracking. The goal of this paper is to review the state-of-the-art tracking methods based on sparse coding. We first analyze the benefits of using sparse coding in visual tracking and then categorize these methods into appearance modeling based on sparse coding (AMSC) and target searching based on sparse representation (TSSR) as well as their combination. For each categorization, we introduce the basic framework and subsequent improvements with emphasis on their advantages and disadvantages. Finally, we conduct extensive experiments to compare the representative methods on a total of 20 test sequences. The experimental results indicate that: (1) AMSC methods significantly outperform TSSR methods. (2) For AMSC methods, both discriminative dictionary and spatial order reserved pooling operators are important for achieving high tracking accuracy. (3) For TSSR methods, the widely used identity pixel basis will degrade the performance when the target or candidate images are not aligned well or severe occlusion occurs. (4) For TSSR methods, ℓ1ℓ1 norm minimization is not necessary. In contrast, ℓ2ℓ2 norm minimization can obtain comparable performance but with lower computational cost. The open questions and future research topics are also discussed.

► A comprehensive review of visual tracking based on sparse coding.
► Extensive experimental comparison between 15 state of the art tracking methods on a total of 20 challenging sequences.
► Analyze the benefits of using sparse coding in visual tracking.
► Point out the future research topics.