# RAMC: A Rotation Adaptive Tracker with Motion Constraint for Satellite Video Single-Object Tracking
## 4 Experimental Details and Analysis
### Comparisons on Satellite Video Datasets  
#### 4.3.1 Quantitative Evaluation  
We compare the proposed RAMC with 13 competing algorithms: KCF [10], SAMF [53], Staple [11], BACF [12], ECO [20], SiamRPN [7], STRCF [54] SiamRPN++ [8], ASRCF [55], LDES [26], GFS-DCF [56], AutoTrack [57], and CFME [36]. Table 4 summarizes the characteristics and experimental results for these trackers, sorted by the AUC of the success plot. Figure 9 shows the average success and precision plots. The proposed RAMC performs optimally with an AUC of 0.785 in the success plot and 0.946 in the precision plot. Algorithms that cannot cope with object rotation (such as KCF, SAMF, AutoTrack, and CFME) generally achieve inferior performances. The baseline KCF achieves the worst performance owing to the limited representation of the HOG. AutoTrack outperforms SAMF by 23.3% and 21.4% in the success and precision plots, respectively, by exploiting local and global information. CFME obtains AUC of 0.613 and 0.855 in the success and precision plots, respectively, because it uses the motion model to mitigate tracking drift. However, they cannot adapt to object rotation. Generally, algorithms that can cope with rotation but cannot cope with tracking drift (such as SiamRPN, GFS-DCF, LDES, ECO, and SiamRPN++) improve tracking performance. SiamRPN, GFS-DCF, ECO, and SiamRPN++ use rotation-invariant deep features to achieve satisfactory performance. However, these algorithms ignore the motion information hidden in adjacent frames and encounter tracking drift. Compared with the champion ECO of the VOT2017 challenge, RAMC produces a gain of 13.4% and 0.7% in the success and precision plots, respectively. Compared with SiamRPN++, which uses deep networks and a multi-layer aggregation mechanism, RAMC achieves a 6.2% higher success rate owing to the consideration of angle and motion information. The results suggest that RAMC can synergize the AE of the rotation branch and the OFR of the translation branch to cope with object rotation and tracking drift issues, yielding superior tracking effects. Meanwhile, it can run at over 40 FPS. The frame rate of SVs is usually 10 FPS. A tracker with a speed higher than 20 FPS can be considered as a real-time algorithm [38; 39]. To further evaluate all the algorithms, we conducted two sets of experiments on datasets with rotation (Rotation) and without rotation (Nrotation). Table 5 summarizes the experimental results, and Figure 10 shows the success and precision plots. For the Rotation dataset, the proposed RAMC obtains better accuracy than that of KCF, SAMF, AutoTrack, and CFME due to considering the rotation issue. The LDES can estimate the rotation angle but ignores the inter-frame motion information. In addition, slight background jitter can affect its accuracy in estimating the angle of small-sized objects. Compared to ECO, ASRCF, and GFS-DCF, which use rotation-invariant deep features, the proposed RAMC exceeds them by 18.9%, 17.5%, and 14% in the success plot, respectively. RAMC has the highest AUC of 0.796 in the success plot, followed by SiamRPN++ (0.732) in second  
Figure 8: Success plot (**a**) and precision plot (**b**) of the variant trackers on datasets. The values in the legends are the AUC. “OPE” = one-pass evaluation, which initializes a tracker in the first frame and lets it run to the end of the sequence. place, and SiamRPN (0.716) in third place. This is because it considers inter-frame motion information on top of the AE, resulting in optimal performance. Compared with SiamRPN, RAMC yields a reduction of only 0.6% in the precision plot. This is because the complex background affects the direction of the optical flow vector, causing motion constraint bias. For the Nrotation dataset, RAMC obtains top-ranked results with AUC of 0.774 and 0.928 in the success and precision plots, respectively. SiamRPN++ produces a satisfactory performance. However, it uses only the appearance information of objects while ignoring motion information.