Improved kernelized correlation filter algorithm and application in the optoelectronic tracking system

Abstract

In order to improve the tracking accuracy and real-time performance of the optoelectronic tracking system, an improved kernelized correlation filter approach is developed to obtain precise tracking of a maneuvering object. The proposed strategy contains merits of adaptive threshold approach, kernelized correlation filter method, and Kalman filter algorithm. The adaptive threshold approach can choose the suitable threshold in accordance with the size of the target in the image to improve the tracking performance of the kernelized correlation filter method. When the change between previous position and current position is larger than the distance threshold, Kalman filter algorithm is used to predict the target position for tracking. The tracking accuracy of the proposed algorithm is improved by updating the prediction of the target position with a trusted algorithm. The experimental results on comparison with some state-of-the-art trackers, such as kernelized correlation filter; Tracking-Learning-Detection; scale adaptive with multiple features; minimum output sum of squared error; and dual correlation filter, demonstrate that the proposed approach has the effectiveness of tracking accuracy and real-time performance in tracking the maneuvering object.