# Learning Spatial-Temporal Background-Aware Based Tracking
###### Abstract  
Discriminative correlation filter (DCF) based tracking algorithms have obtained prominent speed and accuracy strengths, which have attracted extensive attention and research. However, some unavoidable deficiencies still exist. For example, the circulant shifted sampling process is likely to cause repeated periodic assumptions and cause boundary effects, which degrades the tracker's discriminative performance, and the target is not easy to locate in complex appearance changes. In this paper, a spatial-temporal regularization module based on BACF (background-aware correlation filter) framework is proposed, which is performed by introducing a temporal regularization to deal effectively with the boundary effects issue. At the same time, the accuracy of target recognition is improved. This model can be effectively optimized by employing the alternating direction multiplier (ADMM) method, and each sub-problem has a corresponding closed solution. In addition, in terms of feature representation, we combine traditional hand-crafted features with deep convolution features linearly enhance the discriminative performance of the filter. Considerable experiments on multiple well-known benchmarks show the proposed algorithm is performs favorably against many state-of-the-art trackers and achieves an AUC score of 64.4% on OTB-100. Keywords:boundary effects; spatial-temporal regularization; discriminative correlation filter +
Footnote †: journal: Medical College, Quanzhou Medical College, Quanzhou 362011, China; [EMAIL_ADDRESS]  
Footnote †: journal: Medical College, Quanzhou Medical College, Quanzhou 362011, China; [EMAIL_ADDRESS]  
Footnote †: journal: Medical College, Quanzhou Medical College, Quanzhou 362011, China; [EMAIL_ADDRESS]  
Footnote †: journal: Medical College, Quanzhou Medical College, Quanzhou 362011, China; [EMAIL_ADDRESS]  
Footnote †: journal: Medical College, Quanzhou Medical College, Quanzhou 362011, China; [EMAIL_ADDRESS]  
Footnote †: journal: Medical College, Quanzhou Medical College, Quanzhou 362011, China; [EMAIL_ADDRESS]  
Footnote †: journal: Medical College, Quanzhou Medical College, Quanzhou 362011, China; [EMAIL_ADDRESS]  
Footnote †: journal: Medical College, Quanzhou Medical College, Quanzhou 362011, China; [EMAIL_ADDRESS]  
Footnote †: journal: Medical College, Quanzhou Medical College, Quanzhou 362011, China; [EMAIL_ADDRESS]  
Footnote †: journal: Medical College, Quanzhou Medical College, Quanzhou 362011, China; [EMAIL_ADDRESS]  
Footnote †: journal: Medical College, Quanzhou Medical College, Quanzhou 362011, China; [EMAIL_ADDRESS]  
Footnote †: journal: Medical College, Quanzhou Medical College, Quanzhou 362011, China; [EMAIL_ADDRESS]  
to improve the robustness of the tracker. Although the DCF based methods have been extensively refined, there still exist some imperfections. First, the circulant shifted sampling process on the DCF based framework always suffers unwanted boundary effects, which can easily lead to model drift and deteriorate tracking performance. Second, the negative training samples surrounding the target object have not been exploited adequately, and it is difficult to extract target information from similar backgrounds, and thus complicated appearance changes further degrade the robustness of the learned filters. Third, most traditional DCF based trackers usually employ simple shallow-lever hand-crafted features to represent the target. These features always find it tough to capture higher-lever semantic information, and thus cannot locate the target accurately. These imperfections will suppress the discriminative performance of the DCF based tracker from different angles. In recent years, there are a variety of trackers that have been developed to solve the above-mentioned boundary effect problem from different angles. Among them, the SRDCF tracker [22] restrains the filter coefficients through the spatial regularization term, which makes the filter learn in a larger area of space and thus effectively alleviates unwanted boundary effects.