关键词: 视觉目标跟踪, 相关滤波器, 目标重检测

Abstract: To cope with the problem that the traditional Kernelized Correlation Tracker （KCF） lack of detection for tracking failure, an improved KCF tracking method is proposed. First of all, the improved KCF tracker intercepts the training sample with Gaussian window method on the target location, and this sampling method can obtain more effective signal to noise ratio and at the same time reduce the introduction of background interference information, so that the tracker can have stronger adaptability under complex scene. Then, in the process of video target tracking, the new tracker detects whether target tracking is failure by calculating the correlation peak sidelobe ratio, and the target detector learns in the frame with higher value of correlation matching in kernel function. Once tracking failure is detected, the detector will correct the tracker to restore target tracking. The experiment verifies the robustness of improved algorithm of this paper, shows that the overall performance of this new tracker is improved by 13.2%, compared with the traditional KCF tracker.

Key words: visual object tracking, correlation filters, object redetection