Abstract: 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 deformations, and target occlusions, respectively. Furthermore, compared to the original  TLD algorithm, the proposed algorithm significantly increases the video processing frame rate.

Key words: target tracking, tracking-learning-detection(TLD), kernelized correlation filter(KCF), feature extraction, fusion algorithm