An improved Kernelized Correlation Filter tracking algorithm based on multi-channel memory model

摘要

Aiming at the problems of serious occlusions, deformations, background clutters and so on in the process of target tracking, an improved Kemelized Correlation Filter (KCF) tracking algorithm based on multi-channel memory model is proposed in this paper. Firstly, an updating model based on multi-channel memory is established, in which a control channel is used for memorizing target template, and two executive channels are used for memorizing the parameters and feature of classifier. Then, the established multi-channel memory model is introduced into the updating process of classifier. Our experimental results show that the proposed algorithm can achieve accurate and robust target tracking under the conditions of occlusions, deformations and background clutters.