Kernel-Correlated Filtering Target Tracking Algorithm Based on Multi-Features Fusion

When the color of the target is similar to the color of the background or illumination of a scene, scale, and shape of the target change significantly, the traditional kernel-correlated filtering (KCF) algorithm often fails to track the target. Therefore, a multi-features fusion target tracking algorithm based on KCF is proposed. The algorithm makes the most of the illumination and rotation invariance of local binary patterns (LBP) feature, the effective capability of extracting color and contour information from the target by color Name (CN) feature and histogram of oriented gradient (HOG) feature, respectively. The multi-features linear weighted fusion rule is established by calculating the HOG, LBP, and CN histograms of the current frame, and calculating the weighted fusion coefficients of the normalized histograms according to the variance of each histogram adaptively. On this basis, multi-scale sampling is used in the target areas and bilinear interpolation is used to adjust the sample size, which can improve the tracking robustness of the KCF algorithm to adapt to the target scale. The comparison experimental results of 50 video data sets in OTB-2015 show that the average success rate and precision of the proposed algorithm are 24.74% and 8% higher than that of the KCF tracking algorithm, respectively, and also significantly better than the other tracking algorithms ranked top ten in OTB-2015, UAV123, and LaSOT data sets, respectively.