###### Abstract  
In order to address the problems of in-plane rotation and fast motion during near-infrared (NIR) video target tracking, this study explores the application of capsule networks in NIR video and proposes a capsule network method based on background information and spectral position prediction. First, the history frame background information extraction module is proposed. This module performs spectral matching on the history frame images through the average spectral curve of the groundtruth value of the target and makes a rough distinction between the target and the background. On this basis, the background information of history frames is stored as a background pool for subsequent operations. The proposed background target routing module combines the traditional capsule network algorithm with spectral information. Specifically, the similarity between the target capsule and the background capsule in the spectral feature space is calculated, and the capsule weight allocation mechanism is dynamically adjusted. Thus, the discriminative ability of the target and background is strengthened. Finally, the spectral information position prediction module locates the center of the search region in the next frame by fusing the position information and spectral features of adjacent frames with the current frame. This module effectively reduces the computational complexity of feature extraction by capsule networks and improves tracking stability. Experimental evaluations demonstrate that the novel framework achieves superior performance compared to current methods, attaining a 70.3% success rate and 88.4% accuracy on near-infrared (NIR) data. Meanwhile, for visible spectrum (VIS) data analysis, the architecture maintains competitive effectiveness with a 59.6% success rate and 78.8% precision. near-infrared hyperspectral; video tracker; capsule network; spectral information; background information +
Footnote †: journal:However, traditional visible light target tracking [13; 14; 15; 16] technology faces many challenges in practical applications. Since the visible light band (VIS) is greatly affected by lighting conditions, the tracking effect of targets in weak light, complex backgrounds, or occlusion is often not ideal, which seriously limits the application scenarios of target tracking technology. These limitations have led to the application of hyperspectral imaging technology for target tracking [17; 18]. Hyperspectral images (HSI) [19; 20] are three-dimensional data cubes collected by hyperspectral cameras, which can capture image data in multiple bands of the electromagnetic spectrum, usually covering visible light [21; 22; 23], NIR [24; 25; 26], short-wave infrared [27], and other ranges. Each pixel has a complete spectral curve, which reflects the spectral characteristics of the material. The complete spectral curve associated with each pixel represents the inherent spectral characteristics of the material. Among them, the near-infrared band (NIR) [28] generally refers to the electromagnetic spectrum with a wavelength range of about 700 nm to 2500 nm [29]. This band is between the red light end of visible light (about 700 nm) and the mid-infrared band (about 2500 nm). Compared with the visible light band, the NIR has a stronger penetration ability and can capture clear images in haze [30], low light [31], or even completely dark environments. In addition, the NIR has a higher degree of differentiation in the reflective characteristics of different materials and objects, making the segmentation and identification of targets in complex scenes more accurate. The spectral characteristics of near-infrared hyperspectral images (NIR-HSI) are relatively stable and less affected by changes in illumination. This consistency in spectral characteristics, even under varying illumination conditions, enhances the stability and reliability of target tracking. Therefore, NIR-HSI has demonstrated substantial importance in computer vision applications.