关键词: 核相关滤波器（KCF）算法, 异常值检测, 目标丢失, 重检测

Abstract: Aiming at the problem that the traditional Kernel Correlation Filter（KCF） tracking algorithm will take the background information as a target to keep tracking but can not relocation the target, when the target is missing due to illumination variation, severe occlusion and out of view. On the basis of KCF, this paper introduces the outlier detection method as the target loss early warning mechanism, and proposes the target loss re-detection mechanism. This method detects the peak value of the response of each frame, if the abnormal peak value is found, the target is lost or will be lost. Then, the early warning mechanism warns, the target template update is stopped, the target loss re-detection mechanism is started, and search the target in the full frame. The experimental results show that the precision of the improved algorithm is 0.751, and the success rate is 0.579, which is 5.77% and 12.43% higher than that of the traditional KCF tracking algorithm, respectively. This solves the problem that the KCF tracker can not recover the target to keep tracking after the target is lost, the performance of the tracking algorithm is improved and the long-term tracking is realized.

Key words: Kernel Correlation Filter（KCF） algorithm, outlier detection, target loss, re-detection