## 4 Results and Analysis
### Comparative Experiments
These two metrics are significantly ahead of the other six compared algorithms. To better quantify and visualize the advantages of our algorithms, Table 3 details the specific scores of each algorithm under the 19 challenging scenarios faced by the VIS dataset. Our tracker demonstrates superior tracking results against the complex challenges of BC, IPR, LR, OCC, OPR, and SV, fully proving its robustness and adaptability. It ranks second among trackers in DEF, FM, and LL challenges. Our algorithm demonstrated excellent performance in the BC challenge, achieving a success rate of 60.2%. In comparison, SiamCAR ranks second with a success rate of 59.2%, mainly in the interference processing of target information in the BC challenge. It is worth noting that our algorithm demonstrates its unique advantages in complex scenarios such as IPR, OCC, OPR, and SV. Unlike the TransT algorithm, which only relies on contextual information, we fuse the background information of historical frames with the spectral features of the target. This strategy allows us to achieve significant performance improvements of 1.8%, 0.7%, 2.1%, and 1.3%, respectively, when facing the above challenges. However, under the DEF and LL challenges, the TransT algorithm is able to capture detail changes more acutely due to its built-in attention mechanism. Our algorithm faced a certain degree of challenge in DEF and LL, with a slight decrease in success rate of 0.1% and 1.1%. When faced with the FM challenge, our algorithm was unable to accurately capture the background due to its large variations, and the success rate only reached second place. This suggests that we need to further optimize the algorithm to better adapt to complex and changing environmental conditions in future research. To demonstrate the broad applicability of the proposed algorithm, we performed cross-dataset validation using the VOT-NIR dataset and evaluated it using the same metrics as the HOT2023 dataset. Table 4 shows the accuracy, success rate, and running speed (FPS) of all comparative algorithms on all video sequences in the VOT-NIR dataset. Figure 8: Quantitative experimental results of all sequences in the VIS dataset. As shown in Table 4, the proposed algorithm achieves the best success rate and accuracy, with a success rate of 0.699 and a precision of 0.872, which are 5.8% and 1.5% higher than the second-place method, respectively. Compared to the latest Siamese network-based tracker, SiamGAT, the proposed algorithm improves the success rate by 6.2% and the precision by 1.5%. SiamGAT enhances the Siamese framework by introducing a graph attention mechanism that establishes part-to-part correspondence between the target and search regions, effectively overcoming the limitations of traditional cross-correlation methods. However, it does not fully exploit spectral information. In contrast, our algorithm fully leverages the spectral dimension and background information through a series of carefully designed modules. Compared to the Transformer-based tracker TransT, the proposed algorithm improves the success rate by 5.8% and the precision by 4.9%. TransT replaces traditional correlation methods with attention-based modules, alleviating the issue of semantic information loss and overcoming the limitations of local matching. However, TransT performs poorly when handling OCC or SV. The SIP module we designed utilizes the position and spectral information of neighboring frames and the current frame to determine the search area for the next frame, effectively addressing the challenges of OCC and SV. Despite the inherent complexity of the proposed capsule network algorithm, which results in a higher computational load, its running speed reaches 42.6 FPS, meeting the real-time requirements of 25 FPS in typical video scenarios.