## 4 Experiments
### Overall Performances  
To indicate the performance improvements of our approach, we compare Staple_SCA and SAMF_SCA trackers with five recent state-of-art trackers that include STAPLE _CA [20], SAMF_CA [20], SRDCF [19], DSST [17], KCF [15] on the OTB-100 datasets. Figure 5 shows the precision and success plots of proposed tracker and other methods. It is apparent from the success and precision plots of Figure 5 that our proposed trackers have better comprehensive performance than other state of the art trackers. As shown in Figure 5, the success rate and precision rate of the Staple_SCA tracker are 61.6% and 83.7% respectively, which demonstrates the outstanding performance of the proposed method in the visual tracking challenge. Comparing to KCF tracker [15], the proposed tracker gets a 29.1% and 20.4% improvement for the success rate and precision rate, respectively. Furthermore, compared with the conventional Context-aware-based tracker, STAPLE _CA tracker [20], the proposed tracker outperforms it by 3% and 3.3% on success rate and precision rate, respectively. For qualitative evaluation, we compared our proposed trackers with five state of the art trackers in several challenging situations, such as occlusion, background clutters, fast motion, deformation, motion blur, etc. Figure 6 shows that proposed tracker achieves favorable performance under different challenging scenarios. Specifically, in Panda sequences (show in Figure 5(e)), our trackers are the only two that have completed the tracking task successfully. However, we also observe that our trackers are unsuccessful in achieving Bird1 sequences, same as other trackers. This failure is caused by the long-term occlusion, trackers are hard to retrieve long missing target in a limited searching window. Figure 5: Success plots (**a**) and precision plots (**b**) of proposed tracker against five state-of-art trackers [15; 17; 19; 20] on the 100 video sequences in Visual Tracking Benchmark [8]. Figure 6: Comparison of our trackers with other State-of-art trackers [15,17,19,20] in different challenging situations. (**a**) couple; (**b**) coke; (**c**) freeman4; (**d**) football1; (**e**) Panda; (**f**) lemming; (**g**) shaking; (**h**) bolt; (**i**) Bird1. For attribute-based analysis, we evaluate the overall performance of seven competing trackers under 11 sub-categories tagged in the OTB-100. Figure 7 shows the precision plots of different challenging sub-categories on the OTB-100 datasets for seven competing trackers. Impressively, our proposed Staple_SCA tracker obtains ten the best and one the second places (the first place in this sub-category is our SAMF_SCA tracker.) in 11 sub-categories tasks. In the background clutter attribute (Figure 7b), trackers using context-aware framework outperform other trackers. It demonstrates that incorporating context information into the filter can mitigate the drifting problem led by background clutter, and our proposed context selection strategy is more effective than conventional method. Furthermore, the proposed tracker exceeds other trackers obviously in occlusion task. The result suggests that the proposed structural correlation filter produces the desired effect in the occlusion challenge. Figure 7: Precision plots over the default 100 video sequence in Visual Tracking Benchmark [8] for seven competing trackers under 11 challenging attributes. (**a**–**k**) indicate the precision plots of different sub-categories, respectively. Furthermore, in order to compare the effectiveness with the conventional CA method comprehensively, we apply our method to Staple, SAMF, DCF, and MOSSE trackers like [20]. Figure 8 shows the comparing result. Compared to Staple_CA, SAMF_CA, DCF_CA, and MOSSE_CA, our method has improved performance by 3%, 4.9%, 3.7%, and 1.6% on the success plots, respectively. And the precision plots have been improved by 3.3%, 3.5%, 2.7%, and 3.2%, respectively.