Bibliographic Details
| Title: |
基于 LAB 和 HOG 特征的 KCF-TLD 融合目标跟踪算法. |
| Alternate Title: |
A KCF-TLD fusion target tracking algorithm based on LAB and HOG feature. |
| Authors: |
吴小龙1,2 3120230076@bit.edu.cn, 李雪松2 lixuesong@cetc.com.cn, 丁 艳1 dingyan@bit.edu.cn, 罗子娟2 luozijuan@cetc.com.cn, 张博智1 zhangbozhi93@126.com |
| Source: |
Computer Engineering & Science / Jisuanji Gongcheng yu Kexue. Mar2026, Vol. 48 Issue 3, p512-560. 9p. |
| Subjects: |
Object tracking (Computer vision), Tracking algorithms |
| Abstract (English): |
To address the issues of the kernelized correlation filter (KCF) algorithm being susceptible 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, enhancing 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, computationally 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 provide 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 deformaions, and target occlusions, respectively. Furthermore, compared to the original TLD algorithm, the proposed algorithm significantly increases the video processing frame rate. [ABSTRACT FROM AUTHOR] |
| Abstract (Chinese): |
针对核相关滤波(KCF)算法易受环境亮度、目标形变和目标遮挡影响和跟踪-学习-检测 (TLD)算法求解速度慢的问题,提出了基于LAB和HOG 特征的KCF-TLD 融合目标跟踪算法。利用 LAB和HOG特征代替图像样本参与相关滤波运算,提升KCF算法对于环境亮度变化和目标形状变化 的适应能力;用改进的KCF算法代替TLD算法的跟踪器部分,可避免时间复杂度高的光流计算,以提升 TLD算法的计算效率;同时,TLD算法的检测器能在目标遮挡时为相关滤波器提供初始化样本,以实现 对遮挡目标的复跟踪。使用OTB-100开源数据集进行对比验证,与原始的KCF算法相比,所提算法在环 境光照变化、目标形变和目标遮挡下的跟踪精度分别提高了14.6%,12.1%和17.5%;与原始TLD算法 相比,所提算法的视频处理帧率显著提高. [ABSTRACT FROM AUTHOR] |
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| Database: |
Engineering Source |
