Color-adaptive object tracking algorithm based on kernel correlation filter

Aiming at the problem that the fusion of the gradient histogram (HOG) feature and the color histogram feature in the Staple algorithm cannot be adaptively optimized, the paper proposes an improved color adaptive kernel-correlation filtering object tracking algorithm, namely the Stronger-Staple algorithm ( STR-Staple for short). First, the paper uses the object likelihood function to obtain the color histograms of the object and the background, respectively, and uses the Bhattacharyya coefficient to measure the similarity of the color histogram of the object and the background in real time to achieve the tracking and monitoring of each frame of image. Second, An adaptive fusion coefficient is proposed, and the similarity and the fusion coefficient are associated to match the corresponding weights of the features of each frame to achieve the optimal fusion of the algorithm. Finally, the algorithm in this paper are compared with the five popular tracking algorithms on the two data sets OTB-13 and OTB-15. The experimental results show that the algorithm has high robustness under lighting changes, scale changes, occlusion, deformation, background clutter, etc., and its tracking accuracy and success rate are 0.889 and 0.880 in the OTB-13 data set, respectively. In the OTB-15 data set, they are 0.741 and 0.644.which are better than other algorithms.