关键词: 核相关滤波, 目标丢失, 尺度变化, 遮挡, 支持向量机

Abstract: An improved kernel correlation filtering target tracking algorithm is proposed by Kernelized Correlation Filtering（KCF） tracking algorithm, which can not solve the problem of scale change and out-of-view in target tracking. Firstly, a scale filter is added to improve the target scale change based on training translation filter. In order to solve out-of-view problem, the occlusion processing mechanism is used. When the target is not completely occluded, the SVM is used to train the samples, and the re-detection classifier is adopted to detect. Experimental results show that the tracking accuracy of this method is obviously improved in comparison with other excellent tracking algorithms.

Key words: kernel correlation filter, out-of-view, scale change, occlusion, support vector machine