计算机工程与科学2026，Vol.48Issue(3)：512-520,9.DOI:10.3969/j.issn.1007-130X.2026.03.013
基于LAB和HOG特征的KCF-TLD融合目标跟踪算法
A KCF-TLD fusion target tracking algorithm based on LAB and HOG feature
摘要
Abstract
To address the issues of the kernelized correlation filter(KCF)algorithm being suscepti-ble to environmental illumination changes,target deformations,and target occlusions,as well as the slow solution speed of the tracking-learning-detection(TLD)algorithm,a KCF-TLD fusion target tracking algorithm based on LAB and HOG(histogram of oriented gradients)features is proposed.This algorithm utilizes LAB and HOG features instead of image samples for correlation filter operations,en-hancing the KCF algorithm's adaptability to changes in environmental illumination and target shape.By replacing the tracker component of the TLD algorithm with an improved KCF algorithm,computation-ally intensive optical flow calculations with high time complexity can be avoided,thereby improving the computational efficiency of the TLD algorithm.Meanwhile,the detector in the TLD algorithm can pro-vide initialization samples for the correlation filter when the target is occluded,enabling the re-tracking of occluded targets.Comparative validation was conducted using the OTB-100 open-source dataset.Compared to the original KCF algorithm,the proposed algorithm improves tracking accuracy by 14.6%,12.1%,and 17.5%under conditions of environmental illumination changes,target deforma-tions,and target occlusions,respectively.Furthermore,compared to the original TLD algorithm,the proposed algorithm significantly increases the video processing frame rate.关键词
目标跟踪/跟踪-学习-检测(TLD)/核相关滤波(KCF)/特征提取/融合算法Key words
target tracking/tracking-learning-detection(TLD)/kernelized correlation filter(KCF)/feature extraction/fusion algorithm分类
信息技术与安全科学引用本文复制引用
吴小龙,李雪松,丁艳,罗子娟,张博智..基于LAB和HOG特征的KCF-TLD融合目标跟踪算法[J].计算机工程与科学,2026,48(3):512-520,9.基金项目
信息系统工程重点实验室开放基金(05202205) （05202205）
