Near-Infrared Hyperspectral Target Tracking Based on Background Information and Spectral Position Prediction

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.