# rStaple: A Robust Complementary Learning Method for Real-Time Object Tracking
## 5 Experimental Results
### Specific Analysis  
In order to analyze more intuitively, we display the specific tracking results of tested algorithms in five video sequences from OTB2013 and five video sequences from OTB2015. The challenging attributes of these sequences are shown in Tables 7 and 8. Figure 7 shows the tracking results of the proposed algorithm and nine compared trackers on five video sequences from OTB2013. In the first sequence _Girl_, CT and CSK both lose the target before the 400\({}^{th}\) frame. Occlusion occurs between the 430\({}^{th}\) frame and 460\({}^{th}\) frame. Since the obstruction is similar to the target in color and appearance, Staple, KCF, DSST, and fDSST all drift when the occlusion object leaves. All the tracking bounding boxes follow the occluded object and leave the real target. Our approach successfully avoids model drift when the target is occluded based on the adaptive model update. In the second sequence _Jogging-2_, the target has a short-term out of view between the 50\({}^{th}\) frame and 65\({}^{th}\) frame. Only SRDCF, DLSSVM, and rStaple keep tracking when the target reappears at the 65\({}^{th}\) frame; the other trackers all fail to track the target again. The scene of the third sequence, _Matrix_, is relatively complicated: the target is small and undergoing fast motion and the background changes drastically. From the 20\({}^{th}\) frame, CSK, CT, KCF, and DSST fail to track because of the frequent background variance. Staple, LCT and SRDCF lose the target due to fast motion and background clutter. There is also an easily neglected tracking object in the fourth sequence _Skiing_. The searching area of the correlation filter is related to the size of the estimated bounding box in the current frame, so it is quite difficult to track accurately when the target moves fast and the size of searching area is small. Since DLSSVM abandons correlation filters, only the proposed rStaple and DLSSVM keep tracking till the end. In the last sequence _Soccer_, DSST fails to track due to fast motion around the 60\({}^{th}\) frame. Both CSK and LCT lose the target in the 90\({}^{th}\) frame due to occlusion and severe background clutter. In the 110\({}^{th}\) frame, the target is completely occluded by the red background and the colors of most areas in the image are very similar, thus, most trackers cannot detect the target precisely. Only rStaple, fDSST, and SRDCF can follow the target when it reappears in the 135\({}^{th}\) frame. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Sequence** & **Attributes** & **Number of Frames** \\ \hline Girl & SV, OCC, IPR, OPR & 500 \\ Jogging-2 & OCC, DEF, OPR & 307 \\ Matrix & IV, SV, OCC, FM, IPR, OPR, BC & 100 \\ Skiing & IV, SV, DEF, IPR, OPR & 81 \\ Soccer & IV, SV, OCC, MB, FM, IPR, OPR, BC & 392 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Attributes of test video sequences on OTB2013. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Sequence** & **Attributes** & **Number of Frames** \\ \hline Bolt2 & DEF, BC & 293 \\ DragonBaby & SV, OCC, MB, FM, IPR, OPR, OV & 113 \\ KiteSurf & IV, OCC, IPR, OPR & 84 \\ Lemming & IV, SV, OCC, FM, OPR, OV & 1336 \\ Panda & SV, OCC, DEF, IPR, OPR, OV, LR & 1000 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Attributes of test video sequences on OTB2015. Figure 8 shows the tracking results of the proposed algorithm and seven compared trackers on five video sequences from OTB2015. In the first video sequence _Bolt2_, ACFN, LCT, SRDCF, and DLSSVM lose the tracking target before the \(10^{th}\) frame due to fast motion and deformation. In the \(30^{th}\) frame, ACFN follows a similar object near the target. SiamFC loses the target in the \(200^{th}\) frame since it is surrounded by several similar objects. In the second sequence _DragonBaby_, the tracking target is a baby's head which has a small size and undergoes a fast motion. Many trackers fail to follow the target because of the fast motion. The target in the third sequence, _KiteSurf_, is also a man's head.