## 5 Experiments
19: Update the scale model \(A_{t}^{scale}\), \(B_{t}^{scale}\) in Equation (14\(b\)). 20:endif
21:until end of the tracking sequences. ```

**Algorithm 1** Proposed tracking approach: start from step \(t\). ### Analyses of Our Approach

To evaluate the performance of our proposed approach with three strategies, we evaluate four versions of our algorithm on OTB-13: Ours-NCN, in which colour names are not combined; Ours-NSP, in which the scale space filter is not utilized; Ours-NHCM, which lacks the high-confidence judgement mechanism; and Ours-N3, in which none of the three strategies are utilized. The characteristics and comparison results are presented in Table 2.

Comparing the trackers of Ours and Ours-NHCM, precision and success have improved by 6.8% and 13.0%. In addition, FPS has increased by 22.6%. Thus, the high confidence judgement mechanism can alleviate the model corruption that is caused by occlusion and other problems, and the strategy of updating when necessary instead of updating every frame has substantially improved the speed. Without combining the colour features, the tracker Ours-NCN shows poorer performance. Compared with Ours-NCN, the precision of Ours increased by 15.7% and the success rate increased by 30.5%, but at the expense of 11 FPS. Ours-N3, as might be expected, performs worst due to the absence of the benefits of the three strategies. Moreover, its speed is the fastest due to its lower complexity. According to Table 2, tracker Ours outperforms all the other versions in terms of precision and success rate, and the speed can reach 65 FPS, which satisfies real-time requirements. As discussed above, the three mechanisms that are proposed in this paper realize satisfactory efficiency and feasibility. ### Overall Performance Evaluation

In this section, we evaluate the performances of the various versions of our approach. For further comparison, we evaluate our algorithm with 7 state-of-the-art trackers, namely, Staple [24], fDSST [19], KCF [25], CN [16], CSK [22], GOTURN [42] and CCOT [43], as listed in Table 3. Among them, Staple, fDSST, KCF, CN, and CSK are correlation-filter-based algorithms, and GOTURN and CCOT are deeper-learning-based algorithms. The data of GOTURN and CCOT are obtained from their original papers. According to Table 3, CCOT outperforms all the other trackers, and compared to our approach, it realizes average improvements of approximately 10.2% in precision on OTB-13 and 5.6% in success on OTB-15. They perform almost the same in terms of success on OTB-13, and the speed of CCOT is only 0.3 FPS, which cannot satisfy the real-time requirements. In contrast, the speed of our algorithm is 65 FPS, which runs more than 216 times faster than CCOT. Moreover, tracker GOTURN, which is also based on deep learning, runs very fast at 165 FPS and is second only to KCF in terms of speed; however, a large disparity in performance is observed compared with our tracker. Our approach realizes average enhancements of approximately 25% in precision and 49% in success. Figure 4 presents the precision and success plots of the top six trackers on both OTB-13 and OTB-15. The first row in Figure 4 presents the comparison results on OTB-13, and the second row in Figure 4 presents the comparison results on OTB-15. These six trackers are all correlation-filter-based algorithms, and their characteristics are presented in detail in Table 4. KCF and CSK only use HOG features to describe the object model, and they do not utilize the three strategies. CN uses colour names to avoid object deformation problems, and fDSST focuses mainly on scale estimation; their speeds are 78 FPS and 54 FPS, respectively. Our tracker fully utilizes the three mechanisms and performs best in terms of most metrics.