###### Abstract
On the other hand, deep learning [32; 33], especially convolutional neural networks (CNN) [34; 35], has had a profound impact on target tracking technology. Traditional target tracking methods rely on manually designed features, such as color or texture, which are unstable in different scenarios. CNNs are able to learn and capture features at multiple levels [36], from simple edges to more abstract semantic content. They have stronger representational power, making the tracking algorithm more adaptable to complex situations such as lighting changes, target deformation, and occlusion [37]. In addition, the end-to-end learning paradigm in deep learning [38; 39] enables direct regression from pixel data to positional predictions in tracking systems, mitigating cumulative inaccuracies caused by fragmented processing stages. This study utilizes a capsule network as the feature extraction network. A capsule network [40; 41] represents an emerging neural architecture designed to overcome the shortcomings of traditional CNNs in capturing spatial hierarchical information and processing complex deformations. To address this challenge, capsule networks have gained increasing adoption in algorithmic frameworks. [PERSON] et al. [42] demonstrated their effectiveness in hyperspectral target classification tasks. [PERSON] et al. [43] improved on this by designing a capsule residual structure. [PERSON] et al. [44] designed a pyramid dense capsule network for tracking purposes. In summary, these studies exhibit limitations in addressing complex target deformations, depth of feature extraction, and real-time performance. This research employs a capsule network framework to develop a near-infrared hyperspectral tracking system, integrating historical contextual analysis and spectral locational forecasting. The principal innovations of our methodology are summarized below:  
1. We propose a historical frame background extraction module (HFBE) to overcome the complex background in NIR hyperspectral target tracking. This module uses spectral information from different historical frames to construct background template features. A mask is generated by averaging spectral data from adjacent areas surrounding the target, enabling coarse separation between the target and background. 2. A background target routing algorithm (BT) is proposed, which combines traditional capsule network algorithms with spectral information from target and background capsules. By computing feature correlations between foreground-background capsule pairs, the framework dynamically increases the weights of capsules with the same features as the background representation. After generating the background response map, the inverse is taken to get the target response map, thus achieving target localization. 3. A spectral information position prediction module (SIP) is designed to determine the search area for the next frame. By integrating the spectral data of adjacent frames and the current prediction data, the spectral changes of the target can be dynamically adapted to improve the robustness of tracking. This paper is divided into five components: Section 2 reviews critical developments in hyperspectral target detection literature. Section 3 details the architecture and implementation mechanisms of the proposed NIR spectral tracker. Section 4 evaluates algorithmic performance across benchmark hyperspectral datasets, including NIR spectral scenarios. Section 5 synthesizes key findings and outlines potential research extensions.