# RAMC: A Rotation Adaptive Tracker with Motion Constraint for Satellite Video Single-Object Tracking
## 4 Experimental Details and Analysis
### Comparisons on Satellite Video Datasets
#### 4.3.2 Qualitative Evaluation  
Figure 11 presents visualization comparisons of the top four trackers. In the Dubai data, only RAMC and SiamRPN++ successfully track the car; however, SiamRPN++ outputs  
Figure 10: Success and precision plots of all algorithms on the Rotation and Nrotation datasets. (**a**) and (**b**) indicate the success plot and precision plot under the Rotation datasets, respectively. (**c**) and (**d**) indicate the success plot and precision plot under the Nrotation datasets, respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Trackers**} & \multicolumn{2}{c}{**Rotation**} & \multicolumn{2}{c}{**Nrotation**} \\ \cline{2-5}  & **Success Plot** & **Precision Plot** & **Success Plot** & **Precision Plot** \\ \hline KCF (TPAMI 2015) & 0.399 & 0.707 & 0.194 & 0.429 \\ SAMF (ECCV 2014) & 0.464 & 0.815 & 0.283 & 0.589 \\ BACF (ICCV 2017) & 0.494 & 0.935 & 0.523 & 0.833 \\ STRC (CVPR 2018) & 0.587 & 0.942 & 0.513 & 0.760 \\ AutoTrack (CVPR 2020) & 0.636 & 0.947 & 0.576 & 0.885 \\ CFME (TGRS 2020) & 0.513 & 0.769 & 0.714 & **0.940** \\ ASRCF (CVPR 2019) & 0.621 & 0.948 & 0.621 & 0.921 \\ SiamRPN (CVPR 2018) & **0.716** & **0.970** & 0.534 & 0.851 \\ Staple (CVPR 2016) & 0.681 & 0.956 & 0.568 & 0.757 \\ GFS-DCF (ICCV 2019) & 0.656 & **0.965** & 0.604 & 0.916 \\ LDES (AAAI 2019) & 0.569 & 0.715 & **0.724** & 0.933 \\ ECO (CVPR 2017) & 0.607 & 0.938 & 0.696 & **0.949** \\ SiamRPN++ (CVPR 2019) & **0.732** & **0.966** & **0.715** & **0.936** \\ RAMC (proposed) & **0.796** & 0.964 & **0.774** & 0.928 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The results of all algorithms on the Rotation and Nrotation datasets. The top three of each metric is bolded.  
axis-aligned bounding boxes that cannot provide the rotation angle of the object. The ECO fails to deal with scale changes and encounters tracking drift. The LDES cannot adapt to the object's fast rotation and similar background and eventually loses the object. In the Boston data, RAMC, LDES, and SiamRPN++ all locate the plane. Moreover, RAMC and LDES can also obtain the object's angle information. The angle estimation effects of RAMC are superior to those of LDES, owing to the motion constraint. The ECO gradually drifts away from the object. RAMC and ECO accurately locate the train in the Vancouver data. Compared with ECO, RAMC can obtain more semantic representations, including the size and motion direction. The bounding boxes of SiamRPN ++ tend to be smaller and drift away from the train. This is because motion blur deteriorates the tracking template of the train, which affects the RPN module migrated from the faster R-CNN [58] to regress to the correct position and scale. LDES incorrectly estimates the scale owing to motion blur. In the other cases shown in Figure 11, RAMC is better at estimating the rotation angle and position of the objects. The quantitative results verify its outstanding performance in estimating the angle and preventing tracking drift.