## 4 Results and Analysis
### Comparative Experiments
At around frame 90, the fast-moving car makes the hypothesis management and validation process of the MHT algorithm and the SiamGAT algorithm unable to stay abreast of rapid changes in goals, affecting tracking accuracy. At around frame 116, the car undergoes a large shape change, causing the SiamCAR algorithm to lose the target in subsequent frames. The third row of Figure 5 shows the tracking results for the rider11 sequence. The rider is traveling on the road from near to far in the frame. In the experiment, around the 48 th frame, the SiamCAR algorithm and the SiamBAN algorithm, which lack the long-time history information to predict the fast-moving trajectory of the target well, affect the stability of the tracking. The last row of Figure 5 shows the tracking result of the rider17 sequence. A pedestrian riding an electric bike slowly appears in the frame from far and near. It is also shown as a small square target around frame 40 due to occlusion, and after frame 65, it is shown as a vertical rectangular target. In the experiments, when running up to about 128 frames, the low resolution image leads to difficulty in extracting the edge and detail-blurring features of the target. This can cause the algorithm to have trouble discriminating the target from the background and impacts the ability to match features accurately. It greatly affects the STARK algorithm, SiamGAT algorithm, and SiamBAN algorithm. Low-resolution images often struggle to capture changes in the overall appearance of the target, making it difficult for the tracking algorithms to accurately estimate the scale size of the target. This also leads to the fact that around frame 160, only our algorithm is able to accurately frame the entire pedestrians and vehicles with little difference from the true values. Figure 5: Qualitative results on NIR four sequences. In the success and precision curve charts presented in Figure 6, a comprehensive performance evaluation of seven different tracking algorithms is performed for the NIR subset of the HOT2023 dataset. This analysis not only includes the precision of the algorithms in predicting the bounding box (success rate) but also carefully explores their positioning precision. As clearly seen in the figure, our proposed tracking algorithm demonstrates excellent performance on the overall video sequence. The success rate is as high as 70.3% while maintaining an accuracy level of 88.4%. Both of these metrics are significantly ahead of the other six compared algorithms and are firmly at the top of the list. As shown in Table 2, to highlight the effectiveness and uniqueness of our proposed algorithm, we further evaluate its performance across various challenging scenarios in the NIR hyperspectral tracking dataset, including FM, LR, OCC, and SV challenges. Unlike existing trackers such as SiamCAR, STARK, and SiamGAT that rely solely on spatial or temporal features, our algorithm fully exploits the spectral dimension and background information through a series of carefully designed modules. Specifically, in the FM challenge, where the target undergoes large displacements, our algorithm achieves the highest success rate of 76.8%. This is largely due to SIP, which predicts the likely target location by leveraging the spectral and positional continuity across frames. In contrast, SiamCAR depends on centrality-based branching, which struggles under large target movements, resulting in a 10.7% lower success rate. When facing the LR challenge, our algorithm outperforms STARK by 12.4%, benefiting from the use of spectral cues rather than just spatial-temporal dependencies. This advantage stems from HFBE, which constructs background templates from previous frames to improve target-background discrimination. Under occlusion scenarios, SiamGAT fails to utilize the spectral signature of the target effectively. In contrast, our algorithm integrates the BT module, which routes background capsules away from the target representation, allowing the model to maintain target localization even when partially obscured.