Visual object tracking algorithm based on correlation filters

In order to solve the scale prediction problem in visual object tracking,a scale estimation strategy is given in the framework of tracking with kernelized correlation filters,the online updates method of the target model in the traditional kernelized correlation filters based tracking scheme is modified,and a multi-scale visual object tracking algorithm is proposed in this paper.At first,the position and scale kernelized correlation filters are obtained by learning the regularized least-squares classifiers.Secondly,we complete the target position and scale detection by finding the maximum output response of the position and scale kernelized correlation filters,respectively.Finally,the target models are online updated.Corresponding experiment is performed on 12 challenging benchmark video sequences.The results show that the proposed algorithm reduces the median center location error by 7.0pixels,improves the performance by 18.3%in the median success rate,and improves the performance by 5.6%in the median distance precision compared with the best one of the other three existing tracking algorithms based on correlation filters.The proposed tracking algorithm is robust to scale changing,illumination variation,pose variation,partial occlusion,rotation,fast motion and other complex scenes,and it has important research value in theory and application.