In order to solve the scale prediction problem in visual object trackin g,a scale estimation strategy is given in the framework of tracking with kernelized correlation filters,the onli ne 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 k ernelized correlation filters are obtained by learning the regularized least-squares classifiers.Secondly,we co mplete the target position and scale detection by finding the maximum output response of the position and scale kerne lized correlation filters, respectively.Finally,the target models are online updated.Corresponding experime nt is performed on 12challenging benchmark video sequences.The results show that the proposed algor ithm reduces the median center location error by 7.0pixels,improves the performance by 18.3% in the median su ccess 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 robu st to scale changing,illumination variation,pose variation,partial occlusion,rotation,fast motion and other co mplex scenes,and it has important research value in theory and application.