Affiliations: School of Information and Electromechanical Engineering, Hunan International Economics University, Changsha, Hunan 410205, China
Correspondence:
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Corresponding author: Yifan Liao, School of Information and Electromechanical Engineering, Hunan International Economics University, Changsha, Hunan 410205, China. E-mail: 36476379@qq.com.
Abstract: The kernel correlation filtering (KCF) tracking algorithm cannot solve the target tracking mesoscale variation and target loss problem. For this, an improved kernel correlation filtering (IKCF) target tracking algorithm is proposed in this paper. A scale filter is added to the training displacement filter to improve the target scale change problem. In order to solve the problem of target loss, the occlusion processing mechanism is combined, when the target is affected by a small occlusion area, the support vector machine (SVM) is used to train the sample online; when the target is occluded, the re-detection classifier is used for detection. The experimental results show that the tracking accuracy of this method is significantly improved compared with other excellent tracking algorithms.
