# An Improved Kernelized Correlation Filter Algorithm for Underwater Target Tracking
## 4 Experimental Results and Analysis  
This section shows numerical examples to validate the generality and effectiveness of the proposed IKCF for underwater target tracking in forward-looking sonar image sequences; the experimental data are the real forward-looking sonar data obtained in Qiandao Lake. The selected image sequences are processed by pseudocolor processing [20] in this paper. To evaluate the performance of the proposed IKCF for underwater target tracking, the normal target, the target with varying scales, and the target that disappears and appears again are selected for experimental comparison, and the proposed IKCF is compared with KCF [16], CSK [14], MOSSE [13], and mean-shift [7] to verify the effectiveness. To demonstrate the effectiveness of the proposed IKCF for tracking the normal target, the 1 st frame to the 60 th frame are selected in forward-looking sonar image sequences. Figure 4 shows the tracking results of the normal target when the frame is 12 th, 25 th, 34 th, and 51 th, respectively. Center position error curves (1 st frame to 60 th frame) of normal target are shown in Figure 5. Location error precision curves (1 st frame to 60 th frame) of the normal target are shown in Figure 6. Overlap precision curves (1 st frame to 60 th frame) of the normal target are shown in Figure 7. The center position error [21] is the Euclidean distance between the real position of the underwater target and the position of the underwater target tracked by the algorithm, which is used to determine the accuracy of the tracking algorithm. The location error precision [16] shows the percentage of correctly tracked frames for a range of distance thresholds. The overlap precision [22] shows the percentage of correctly tracked frames for a range of overlap thresholds. As seen from Figure 4 and 5, the mean-shift, KCF, and MOSSE have serious tracking errors in the tracking process; although the initial tracking error is relatively small, with the frame number increase, the error appears to have an upward trend. The proposed IKCF has a higher tracking accuracy and stability. As further seen in Figure 6, the ratio of the image frame number to the total frame numbers of the proposed IKCF is far superior to that of the other algorithms in the tracking result, and the center position error value of all the image frames is within 6 pixels, while that of the KCF, CSK, MOSSE, and mean-shift algorithms is within 10 pixels, 8 pixels, 14 pixels, and 20 pixels, respectively. Therefore, the proposed IKCF has a higher tracking accuracy. From Figure 7, the location error precision of the proposed IKCF is far superior to that of the other algorithms in the tracking result, and the overlap threshold of all the image frames is within 0.76, while that for the KCF, CSK, MOSSE, and mean-shift algorithms is within 0.64, 0.74, 0.48, and 0.38, respectively. Therefore, the proposed IKCF has a higher tracking accuracy. Figure 4: Tracking results for normal target: (**a**) The tracking results at the 12 th frame; (**b**) the tracking results at the 25 th frame; (**c**) the tracking results at the 35 th frame; (**d**) the tracking results at the 51 st frame. Figure 5: Center position error curves (1 st frame to 60 th frame). Figure 8 shows the tracking results of the target with varying scales when the frame is 1087 th, 1093 th, 1112 th, and 1132 th, respectively. Center position error curves (1082 nd frame to 1152 nd frame) of the target with varying scales are shown in Figure 9. Location error precision curves (1082 nd frame to 1152 nd frame) of the target with varying scales are shown in Figure 10. Overlap precision curves (1082 nd frame to 1152 nd frame) of the target with varying scales are shown in Figure 11.  
Figure 6: Location error precision curves (1 st frame to 60 th frame). Figure 7: Overlap precision curves (1 st frame to 60 th frame).