Scale‐Adaptive Kernel Correlation Filter with Maximum Posterior Probability Estimation and Combined Features for Visual Target Tracking

Although the correlation filtering tracker for visual target tracking has achieved excellent results in both accuracy and robustness, there are still some problems yet to be solved. Obtaining stable scale estimation using traditional trackers is a challenging problem in visual target tracking, and many trackers fail to handle scale change in complex video sequences. In order to solve the problems of scale change, partial occlusion and geometric deformation for target tracking effectively, a new tracker based on kernel correlation filtering is developed in our study. The tracker obtained with maximum posterior probability method has scale adaptive ability and can deal with scale change to improve the tracking ability. In addition, the tracker further enhances the ability to deal with illumination variation, geometric deformation and occlusion by fusing the adaptive color naming feature and the histogram of oriented gradient feature as well. The VOT‐2018 which has 50 video sequences is used as the benchmark data set in this work and the simulation evaluation on this data set have shown that the proposed tracker has achieved stable tracking results in some challenging scenarios and can achieve better tracking performance than other trackers. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.