# Visual Tracking with FPN Based on Transformer and Response Map Enhancement
## 4 Results
### Visual Comparison of Tracking Results  
In order to intuitively show the tracking results, and illustrate the performance improvement in dealing similar object, occlusion and viewpoint change, we chose four image sequences from the UAV123 dataset and OTB100 dataset, which have the above challenges, compared with the baseline tracker SiamCAR and the other two state-of-the-art tracking algorithms.  
Car1: The Car1 sequence is a video sequence with similar object interference and occlusion. First, similar object interference is shown. During 376-516 frames, disturbed by a car nearby, scale chaos appeared in the baseline tracker SiamCAR and the other algorithm. Particularly, in the 452 and 489 frame, the tracking box of DaSiamRPN and SiamRPN++ contained a similar object. Especially, after 516 frames, SiamRPN++ tracking appeared to drift.  
Car1: Next, the role of occlusion is shown. SiamRPN++ drifted due to previous errors. The car began to be occluded at frame 749 until it was fully occluded at frame 781 and finally reappeared in frame 818. Only our proposed method could still locate and track the target effectively without being affected by occlusion.  
Car7: In Car7 sequence, similar object interference occurs simultaneously in the process of occlusion. In frames 55-238, when the car is blocked and similar vehicles appear nearby, a scale error occurred in the baseline tracker SiamCAR and the other methods deviated from the target. After frame 316, when the target reappeared, the coincidence between TR-Siam and ground truth was still very high.  
Box: As shown in Figure 8d, the main difficulty of the Box video sequence is that the Box is partially or completely occluded. In frames 302 to 315, when the target is partially occluded by the vernier caliper, all comparison algorithms can effectively track the target. However, as the difficulty increased, the box was placed behind the vernier caliper in frames 447 to 489 and was completely occluded. The comparison algorithm cannot track the target. Only our proposed algorithm can track the target robustly. It can be seen that the algorithm can still track the target robustly and accurately when the target is completely occluded.  
Girl2: As shown in Figure 8e, in the Girl2 video sequence, the difficulty of the video sequence is that the girl is occluded by similar object. When the target begins to be occluded by similar targets in 107 frames, all algorithms look for the target. When the target is completely occluded in frame 111, tracking drift appears in SiamRPN++. From 111 frames to 129 frames, similar objects keep moving far away from the girl, and a tracking failure occurs in the comparison algorithm. However, the algorithm in this paper can still track the target correctly after the target reappears. This shows that our proposed algorithm  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Tracker** & **AO** & **SR\({}_{0.5}\)** & **SR\({}_{0.75}\)** & **FPS** \\ \hline KCF [12] & 0.203 & 0.177 & 0.065 & 94.66 \\ ECO [14] & 0.316 & 0.309 & 0.111 & 2.62 \\ SiamFC [5] & 0.374 & 0.404 & 0.144 & 25.81 \\ THOR [35] & 0.447 & 0.538 & 0.204 & 1.00 \\ SiamRPN [6] & 0.483 & 0.581 & 0.270 & 97.55 \\ SiamFC++ [35] & 0.493 & 0.577 & 0.323 & 160 \\ SiamRPN++ [7] & 0.518 & 0.618 & 0.325 & 35 \\ ATOM [31] & 0.556 & 0.634 & 0.402 & 30 \\ SiamCAR [18] & 0.569 & 0.670 & 0.415 & 50 \\ Ours & 0.582 & 0.683 & 0.457 & 45 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The evaluation on GOT-10K-TOP-2 results are highlighted in red and blue, respectively.  
can effectively solve the problem of when the target is occluded by a similar object and ensure the robustness of the algorithm.