# rStaple: A Robust Complementary Learning Method for Real-Time Object Tracking
## 5 Experimental Results
### Overall Performance
Compared to Staple, the performance is improved in all attributes, especially in these four attributes: fast motion (9.6%), deformation (7.6%), occlusion (9.1%), and out of view (22.9%). In Tables 5 and 6, our tracker ranks first in eight attributes. Compared to the baseline Staple, our performance is improved in all attributes. According to Tables 3-6, the proposed tracker is extremely apt at handling the occlusion, out of view and scale variation situations. Furthermore, the performance of the proposed tracker in low resolution is relatively poor, which is due to using fHOG because fHOG resizes a 4 \(\times\) 4 pixel area to one pixel for calculating the final feature. Most tracking targets with a low resolution attribute are extremely small, and a host of target information is lost after the resize which leads to tracking failure. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & **Ours** & **LCT** & **SRDCF** & **DLSSVM** & **Staple** & **fDSST** & **DSST** & **KCF** & **CSK** & **CT** \\ \hline DP rate (\%) & **84.8** & **84.8** & _83.8_ & 82.8 & 78.2 & 80.3 & 74 & 74 & 54.5 & 39 \\ \hline OS rate (\%) & _78.7_ & **81.3** & 78.1 & 72.5 & 73.8 & 74.7 & 67 & 62.3 & 44.3 & 26.9 \\ \hline Speed (FPS) & 70.3 & 28.9 & 8 & 12.1 & 86 & 119 & 19.4 & 379 & **738** & 152 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The performance on OTB2013 dataset of nine trackers. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & **Ours** & **CFNet** & **ACFN** & **SiamFC** & **LCT** & **Staple** & **SRDCF** & **DLSSVM** \\ \hline DP rate (\%) & **81.1** & 76.9 & _79.6_ & 77.1 & 76.2 & 78.4 & 78.8 & 76.7 \\ \hline OS rate (\%) & **75.2** & 73.6 & 68.9 & 73 & 70.1 & 69.9 & 72.6 & 62.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The performance on OTB2015 dataset of eight trackers. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & **Ours** & **LCT** & **SRDCF** & **DLSSVM** & **Staple** & **fDSST** & **DSST** & **KCF** & **CSK** & **CT** \\ \hline FM (17) & 73.4 & 66.5 & 74.1 & **74.5** & 64.2 & 72.1 & 51.3 & 60.2 & 38.1 & 23.9 \\ BC (21) & 80.5 & 79.6 & 80.3 & _81.4_ & 73 & **84.2** & 69.4 & 75.3 & 58.5 & 35.3 \\ MB (12) & 70.5 & 66.4 & **78.9** & 75.7 & 67 & _78.5_ & 54.4 & 65 & 34.2 & 23.3 \\ DEF (19) & **89.9** & _87.3_ & 85.5 & 86.9 & 78.8 & 74.8 & 65.8 & 74 & 47.6 & 36.9 \\ IV (25) & **84.3** & 79.2 & 76.1 & 75.9 & 72.7 & _80.4_ & 73 & 72.8 & 48.1 & 31.8 \\ IPR (31) & **81.7** & 80.2 & _76.6_ & _81.4_ & 77.3 & 78.3 & 76.8 & 72.5 & 54.7 & 36.9 \\ LR (4) & 50.8 & 35.2 & _51.8_ & **54.6** & 50.5 & 47.4 & 49.7 & 38.1 & 41.1 & 13.1 \\ OCC (29) & **88.1** & _84.5_ & 84.4 & 82.1 & 77.5 & 76.2 & 70.6 & 74.9 & 50 & 40.1 \\ OPR (39) & **85.5** & _85_ & 81.8 & 83.7 & 76.4 & 77.8 & 73.6 & 72.9 & 54 & 38.2 \\ OV (6) & **79.5** & _72.8_ & 68 & 68.8 & 66.9 & 68.3 & 51.1 & 65 & 37.9 & 22.5 \\ SV (28) & **82.2** & 75.8 & _77.8_ & 76.2 & 72.1 & 76.2 & 73.8 & 67.9 & 50.3 & 40.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Distance precision rate (%) of \(11\) different attributes on OTB2013 dataset. The contribution to performance improvement can be generalized to three aspects. Firstly, we apply significant enhancement to the score map of the color histogram. This improves the attention of the tracker to the target and increases the weight of the color histogram with high confidence, thus weakening the boundary effect. Secondly, the combination of fHOG [6] and CN [7,8] strengthens the discriminating ability of the proposed tracker, which facilitates the translation filter locating the target more accurately. Thirdly, we adaptively update the model of the translation filter and scale filter with criteria _APCE_[44] and \(F_{\text{max}}\). When the target is occluded or out of view, the filter model update is terminated. Therefore, this strategy effectively avoids model drift.