基于LAB和HOG特征的KCF-TLD融合目标跟踪算法OA

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.