红外技术2025，Vol.47Issue(4)：437-444,8.
基于两阶段时空加权特征的红外目标跟踪算法
Infrared Object Tracking Algorithm Based on Two-stage Spatiotemporal Weighted Features
摘要
Abstract
This paper proposes an infrared object tracking algorithm based on two-stage spatiotemporally weighted features.First,the object area is divided into non-overlapping areas of the same size,and different weights are assigned to different location information,from which an adaptive spatiotemporal weighted Bayesian classifier is derived.An improved metric is then used to identify classification samples with the maximum class difference,which have high tracking adaptability,and to enable re-capture and tracking when the target is occluded.Simulation experiments show that,compared with mainstream tracking algorithms such as SiamFC,the proposed algorithm achieves significant improvements in overlap rate and central error indicators on the LSOTB-TIR target tracking dataset,significantly enhancing tracking stability and positioning accuracy.The tracking speed reaches 56 F/s,making it suitable for engineering applications.关键词
红外图像/目标跟踪/压缩感知/空时加权/遮挡检测/贝叶斯分析Key words
infrared image/object tracking/compression sensing/spatiotemporal weighting/occlusion detection/Bayesian analysis分类
计算机与自动化引用本文复制引用
李清忠..基于两阶段时空加权特征的红外目标跟踪算法[J].红外技术,2025,47(4):437-444,8.基金项目
江西省教育厅科学技术研究项目(GJJ202116). （GJJ202116）
