## 5 Experiments
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Trackers** & **Colour** & **Scale Space** & **High-Confidence** & **Precision** & **Success** & **FPS** \\  & **attributes** & **Filter** & **Mechanism** & & & \\ \hline Ours-N3 & No & No & No & 0.682 & 0.458 & 85 \\ Ours-NCN & No & Yes & Yes & 0.705 & 0.512 & 76 \\ Ours-NSP & Yes & No & Yes & 0.746 & 0.577 & 69 \\ Ours-NHCM & Yes & Yes & No & 0.764 & 0.591 & 53 \\ Ours & Yes & Yes & Yes & 0.816 & 0.668 & 65 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The characteristics and comparison results of different versions of our propoach on OTB-13. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Trackers** & **Correlation** & **Deep** & **OTB-13** & **OTB-13** & **OTB-15** & **OTB-13** & **OTB-13** & **FPS** \\  & **Filters** & **Learning** & **Precision** & **Success** & **Precision** & **Success** & **Precision** \\ \hline Staple & Yes & No & 0.767 & 0.617 & 0.769 & 0.594 & 80 \\ KCF & Yes & No & 0.703 & 0.456 & 0.710 & 0.438 & 172 \\ CN & Yes & No & 0.637 & 0.432 & 0.641 & 0.411 & 78 \\ CSK & Yes & No & 0.490 & 0.324 & 0.517 & 0.305 & 151 \\ fDDST & Yes & No & 0.696 & 0.526 & 0.705 & 0.512 & 54 \\ GOTURN & No & Yes & 0.620 & 0.444 & 0.572 & 0.427 & 165 \\ CCOT & No & Yes & 0.899 & 0.672 & - & 0.682 & 0.3 \\ Ours & Yes & No & 0.816 & 0.668 & 0.802 & 0.646 & 65 \\ \hline Average improvement & - & - & 26.9\% & 48.5\% & 22.3\% & 50.3\% & - \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The comparison results with the other 7 state-of-the-art trackers on OTB-13 and OTB-15. CSK is a famous tracker that pioneered the application of correlation filters(CFs) in visual tracking; hence, it is a satisfactory representation of previous classical trackers. Our approach significantly improves CSK, with an average improvement of approximately 60% in precision. KCF adopts cyclic shifts for dense sampling and joins multi-channel HOG features to make the tracker more robust. It can run very fast at 172 FPS. Our tracker significantly outperforms KCF, with average enhancements of the success rate of approximately 46.5% on OTB-13 and 47% on OTB-15. CN utilizes the feature of multi-channel colour names that is baseds on CSK, and fDSST is the speedy version of DSST with a scale space filter. They have realized satisfactory improvements, but without the colour names and high-confidence judgement mechanism, the performance is worse than that of our approach. By combining HOG features and colour names, Staple realizes relatively satisfactory performance. Moreover, on the basis of multi-feature fusion, our tracker employs adaptive scale estimation and a high-confidence judgement mechanism. Our tracker outperforms Staple by 5.3% in terms of precision and 8.5% in terms of the success rate on average. ### Robustness Evaluation

Figure 5 presents a visualization of the tracking results of our tacker and of other famous trackers on various test sequences. The \"Liquor\", \"Skating\", \"Jogging-2\" and \"Subway\" sequences all contain occlusions, deformations, and background clutter, which lead CSK and KCF to miss the object completely, whereas our tracker can track the object accurately. In sequences \"Skating\" and \"Jogging-2\" which contain illumination variations, scale variations, occlusions, deformations, out-of-plane rotations, and background clutter, all trackers identify the target \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Trackers** & **Colour Attributes** & **Scale Space Filter** & **High-Confidence Mechanism** \\ \hline Staple & Yes & Yes & No \\ KCF & No & No & No \\ CN & Yes & No & No \\ CSK & No & No & No \\ fDDST & No & Yes & No \\ Ours & Yes & Yes & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The characteristics of 6 trackers based on correlation filters. Figure 4: The precision and success plots of OPE on OTB-13 and OTB-15. in the first several frames, but most trackers lose sight of the object over time, and only our approach can always track the object correctly.