关键词: 异常值检测, 核相关滤波器（KCF）, 目标丢失

Abstract: The Kernel Correlation Filter（KCF） tracking algorithm will suffer from the phenomenon of target loss when the target is affected by such factors as scale variation, severe occlusion, similar target interference, illumination variation and out of view. Once the target is lost, the KCF tracking algorithm itself is imperceptible, and the tracker will track the background information as a target to keep tracking, resulting in the complete loss of the real target. In order to solve this problem, based on the KCF tracking algorithm, this paper proposes a target loss early warning mechanism based on outlier detection method. The method detects the peak of the response of each frame using the mean and standard deviations of dynamic peak value data of fixed dimensions, and if the abnormal peak is found, the target is lost or will be lost, which solves the problem that loss target can not be perceived during the tracking process. The experimental results indicate that the proposed method can correctly predict the loss of target in the tracking process of KCF algorithm, and the success rate is 100%. The proposed method has high reliability and can provide a reliable basis for when to load target re detection mechanism, after the target is lost.

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