## 4 Results and Analysis
### Comparative Experiments
This leads to a 9.1% improvement in success rate over SiamGAT. For the SV challenge, where spectral variability is high, our tracker maintains a strong 74% success rate by continuously adapting to the spectral characteristics of the target. While MHT exploits material properties to improve robustness, its inability to estimate target scale limits its accuracy, highlighting the superior adaptability and tracking precision of our model. Figure 7 intuitively summarizes the tracking performance of the proposed algorithm on the VIS dataset, using four representative video sequences, in comparison with six other SOTA algorithms. Figure 6: Quantitative experimental results of all sequences in the NIR dataset. The first row of Figure 7 shows the tracking results of the board sequence. As the hand moves, the board appears to rotate planarly, and after 167 frames, a coke bottle on the floor obscures a portion of the board. Around frame 456 in the experiment, cluttered objects in the background (e.g., wires and other electronic components) interfered with the feature extraction and detection of the tracker, causing the SiamGAT algorithm to mistake parts of the background for the target. The second row of Figure 7 shows the kangaroo sequence tracking outcomes. The quick hopping of kangaroos and the resemblance between stalked kangaroos and their fellow kangaroos caused some motion blur and background interference. However, the majority of the trackers show good performance in this sequence as they rely heavily on the target features. Thus, despite the presence of background interference and multiple similar targets, the trackers are able to maintain tracking stability and accuracy for the target kangaroo by recognizing and matching these features. The third row of Figure 7 shows the tracking results for the ball&mirror9 sequence. The target white ball becomes blurred due to reflections in the mirror. In the experiment, at around frame 79, artefacts or unrealistic highlight areas appear in the mirror, which may visually have similar characteristics to the actual target, causing the TransT algorithm to incorrectly track the reflected ball in the mirror. The last row of Figure 7 shows the tracking results for the rainstreet10 sequence. At around frames 48 and 102, the tree and the car in the frame completely occlude or \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Challenges** & **MHT** & **SiamBAN** & **SiamCAR** & **SiamGAT** & **STARK** & **TransT** & **Ours** \\ \hline BC & 0.019 & 0.043 & 0.021 & 0.582 & **0.300** & 0.030 & 0.071 \\ FM & 0.444 & 0.609 & **0.661** & 0.649 & 0.643 & 0.659 & _0.768_ \\ LR & 0.262 & 0.407 & 0.454 & 0.512 & **0.527** & 0.443 & _0.651_ \\ OCC & 0.369 & 0.506 & 0.535 & **0.566** & 0.520 & 0.537 & _0.657_ \\ SV & 0.448 & 0.598 & 0.638 & 0.640 & 0.614 & **0.647** & _0.740_ \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The AUC comparison on NIR/false-color videos. The highest and second-highest values are marked in _red italics_ and **blue bold**, respectively. Figure 7: Qualitative results on VIS four sequences. partially occlude the pedestrians, respectively, and under prolonged or severe occlusion, the tracker may lose track of the target completely. In the experiment, at frame 78, the MHT method and the SiamCAR method lost the target due to the prolonged occlusion of the tree before and failed to retrieve the target in the subsequent frames. In the results of the quantitative experiment presented in Figure 8, a comprehensive performance evaluation of seven different tracking algorithms is performed for a subset of the VIS in the HOT2023 dataset. This analysis not only covers the success rate of the algorithms in the prediction of the bounding box, but also takes a close look at their precision of localization. It is clearly visible from the figure that our proposed tracking algorithms demonstrate excellent performance on the overall video sequences. The success rate is as high as 59.6% while maintaining an accuracy level of 78.8%.