## 4 Results and Analysis  
### Comparative Experiments  
We compared the performance of the proposed algorithm with six state-of-the-art (SOTA) hyperspectral tracking algorithms in this experiment, i.e., MHT [49], SiamBAN [15], SiamCAR [45], SiamGAT [46], STARK [47], and TransT [1]. MHT uses different materials with different spectral information to differentiate between similarly colored objects, and SiamBAN uses a full convolutional network approach to classify the target and background and regress to the target frame. The SiamCAR approach is similar to SiamBAN in that it proposes centrality branches to update the predicted centroids. In contrast to SiamGAT, which introduces a target-aware selection mechanism, SiamCAR exhibits limited adaptability in handling target deformations. STARK uses intermediate frame information as a dynamic template and updates the template based on the transformer architecture. As for TransT, this network effectively fuses templates and searches for region features using only attention. It is worth noting that our proposed method is based on capsule networks and is not directly inspired by any of the compared methods. By comparing the proposed tracker algorithm with these SOTA algorithms, it is our goal to prove its usefulness and contendability in hyperspectral tracking applications. Figure 5 provides a visual summary of the tracking performance of our proposed method, benchmarked against six SOTA approaches across four distinct video sequences from the NIR dataset. In the figure, the various algorithms are clearly distinguished by rectangular boxes of different colors, where sky blue marks our proposed algorithm, green represents the classical MHT algorithm, red denotes the SiamBAN algorithm, pink-purple is the SiamGAT algorithm, blue is the SiamCAR algorithm, yellow highlights the STARK algorithm, black represents the TransT algorithm, and white represents the Groundtruth. Given the numerous rectangular boxes in the figure, it seems challenging to visually assess the strengths and weaknesses of each algorithm directly. We further utilized precision and success plots to quantitatively and intuitively evaluate the tracking performance of each method. These two graphs not only accurately reflect the precision and stability of the algorithms in the tracking process, but also facilitate us to quickly identify which algorithms have the best performance on specific sequences. Through this comprehensive analysis, we can evaluate the advantages and disadvantages of each algorithm in a more scientific way, and offer solid support for the subsequent optimization and deployment of the algorithms. The first row of Figure 5 shows the tracking results for the car11 sequence. A white car is traveling fast on the road. The pedestrian driving a battery-operated car completely obscures the white car at frame 49, which is a great challenge for trackers that rely on material information features for tracking. In the experiment, when running to the 78 th frame, the traveling car undergoes scale deformation due to the limitation of the camera angle, and appears to be nearer and farther away. When the target is occluded, the keypoints on which the STARK algorithm relies may be lost or become invisible, leading to target loss. At the 100 th frame, the car is moving fast, causing the MHT algorithm, SiamBAN algorithm, and SiamGAT algorithm to possibly not accommodate changes in target position and scale adjustments in a timely manner in terms of update frequency and scale adjustments, resulting in degradation of tracking accuracy. The second row of Figure 5 shows the tracking result of the car78 sequence. The black car traveling from near to far is obscured by the tree and flower beds on the side of the road at frame 22. It is only after 45 frames that the full target silhouette appears. This also resulted in tracking drift.