## 4 Results
This video tests the tracker's ability to track changes in the appearance and shape of the target. According to Table 1, the experimental results showed that the algorithms MCCTH, LEDS, and KCF showed good tracking ability in the case of deformation. In scenario 3, the target is a moving pedestrian, moving from the left side of the screen to the right side. During the movement, the outline of the pedestrian appears blurred. This video tests the tracker's ability to track objects with a blurry appearance. According to Table 1, the experimental results showed that the algorithms ADMTCF, KCF, and MCCTH showed good tracking ability in the case of motion blur. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Title 2** & **Ours** & **KCF** & **MCCTH** & **MKCFup** & **LDES** & **SiamRPN++** \\ \hline Scenario 1 & 0.943 & 0.983 * & 0.935 & 0.945 & 0.943 & 0.924 \\ \hline Scenario 2 & 0.914 & 0.935 & 0.944 * & 0.850 & 0.943 & 0.863 \\ \hline Scenario 3 & 0.965 * & 0.933 & 0.879 & 0.866 & 0.873 & 0.852 \\ \hline Scenario 4 & 0.843 & 0.836 & 0.902 & 0.937 * & 0.935 & 0.890 \\ \hline \hline \end{tabular}
* First place.  
\end{table} 
Table 1: Tracking accuracy using Metrics-1. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Title 2** & **Ours** & **KCF** & **MCCTH** & **MKCFup** & **LDES** & **SiamRPN++** \\ \hline Scenario 1 & 0.593 & 0.456 & 0.614 & 0.672 & 0.649 & 0.690 * \\ \hline Scenario 2 & 0.730 & 0.793 & 0.815 * & 0.675 & 0.754 & 0.637 \\ \hline Scenario 3 & 0.581 * & 0.486 & 0.424 & 0.443 & 0.449 & 0.506 \\ \hline Scenario 4 & 0.242 * & 0.117 & 0.123 & 0.081 & 0.122 & 0.237 \\ \hline \hline \end{tabular}
* First place.  
\end{table} 
Table 2: Tracking accuracy using Metrics-2. In scenario 4, the target is a moving pedestrian, moving from the right side to the left. During the pedestrian's movement, the outline of the pedestrian is occluded by a white car. This video tests the tracker's ability to track the occlusion of the target. According to Table 1, the experimental results showed that the algorithms MKCFup, LDES, and MCCTH showed good tracking ability in the case of occlusion. In scenario 1 and scenario 2, due to the position and angle of the camera fixing, the coordinates of the target object did not shift due to camera movement or angle swing. Therefore, we marked the bounding boxes of the various trackers and the tracked trajectories in the images in scenario 1 and scenario 2, as shown in Figures 10 and 11. The trajectories are the lines connecting the center points of the bounding boxes in frames. According to Table 1 and Figure 13, we can observe the tracking performance of various algorithms evaluated using metrics 1. By using HOG features, KCF performed best in scenario 1 (illumination variation). Due to using an adaptively updated multicue analysis framework, MCCTH performed best in scenario 2 (deformation). LDES was second in scenario 2. Our adaptive dynamic multi-template CF performed best in scenario 3. MKCFup was second in scenario 4. According to Table 2 and Figure 14, we can observe the tracking performance of various algorithms evaluated using Metrics-2. SiamRPN++ performed best in scenario 1. MCCTH performed best in scenario 2. Our adaptive dynamic multi-template CF performed best in scenarios 3 and 4. Figure 13: Tracking accuracy using Metrics-1: (**a**) scenario 1; (**b**) scenario 2; (**c**) scenario 3; (**d**) scenario 4. We took the average of the AVG accuracy of the 4 scenarios in Tables 3 and 4 to indicate the tracking ability of the algorithm that encounters illumination variation, deformation, motion blur, and occlusion at the same time, and plots Figure 15a,b. From Figure 15a, it can be seen that our proposed adaptive dynamic multi-template object tracker achieved third place and maintained good tracking stability and robustness when the tracking encountered illumination variation, deformation, motion blur, and occlusion. From Figure 15b, it can be seen that our proposed ADMTCF achieved the highest tracking accuracy of all trackers.