# Visual MAV Tracker with Adaptive Search Region
## 4 Results
### Path Predictive SR  
The PP model was generated by minimizing the location error of the SR in the sequences. To obtain the error, the distance (in pixels) between the center point of the SR and the true center point of the MAV was calculated. Then, the location error was calculated by dividing the distance by the size of the SR. The value of the correction coefficient \(c\) in Equation (1) was determined to be 0.9836 by minimizing the constraint that \(c\) should be greater than or equal to 0. The error in this minimization process is not a tracking error but is a location error of the SR. SC and SO were also adopted during the minimization of PP. The sequences used consisted of approximately 1000 frames (30 FPS). The effects of PP were verified using test sequences. PP significantly reduced the SR localization error in most frames, as shown in Figure 8. Furthermore, large errors were observed frequently in the absence of PP; these large errors can cause tracking failure. However, PP treatment reduced the risk of failure by a remarkable amount. Thus, PP prevents the SR from losing the target object, and the robustness can be improved. Figure 7: Effects of scale ratio on accuracy. The tracking performance of the models was compared with the IOU values of the test sequences, as shown in Figure 9. CSR failed to track across 100 frames in each experiment. Specifically, it failed within 50 frames, except for sequence 1. CSR + SC tracked the MAV for longer than CSR did. However, it also failed at 100-150 frames, indicating the limitation of SC. Although CSR + SC + SO provided robust tracking, its IOU value (i.e., accuracy) fluctuated largely across the frames. PP resolves this unstable tracking. Except for some sections, the IOU of CSR + SC + SO + PP was maintained at a high level (i.e., 0.8-1). PP can prevent tracking failure caused by the fast motions of the MAV. While the truncation from the SR can be prevented by SC for a slowly flying MAV, SC cannot prevent tracking failure if the MAV flies at a high speed. Specifically, when an MAV is truncated from the SR because of its fast motion, the CSR + SC model can prevent over-shrinkage. However, the error in the bounding box caused by truncation remains, as shown in Figure 5. The CSR + SC model determines the center of the SR in the following frame as the center of the current bounding box. Thus, if the target is stationary or moves slowly, this truncation can be removed. However, if the target moves fast, it is continuously truncated (or vanishes) from the SR, which can lead to tracking failure, as shown in Figure 9d,e. In contrast, when PP is applied, it moves the SR such that its center is close to the midpoint of the moving target. Thus, truncation and subsequent failure are prevented by PP. For example, in Figure 9d, failure did not occur even though the accuracy remained low for some frames. Figure 8: Localization error of SR across frames in the test sequences. (**a–e**) represent the error of test sequence 1–5, respectively. PP also provides opportunities for recovering high IOU values that decrease due to misinterpretations of the background in previous frames. For example, as shown in Figure 9d, the IOU started to significantly decrease for both the CSR + SC + SO and CSR + SC + SO + MP models at the 114 th frame. The background is very similar to the MAV in this frame. While the CSR + SC + SO model suddenly fails after this frame, PP prevents the tracker from failing. Although the IOU value remains low for a long interval owing to the distracting background, PP enables the SR to follow the MAV. Afterward, when the background is changed to be less similar to the MAV, the IOU value increases to a high level (i.e., 0.8-1). A series of images and tracking results of the accuracy recovery function are shown in Figure 10. While the CSR + SC + SO model failed to track from the 112 th frame onwards, PP maintained tracking and returned to a high estimation state after 73 frames.