%A Ningning CHEN,Jianwei ZHAO,Zhenghua ZHOU %T Visual tracking algorithm based on verifying networks %0 Journal Article %D 2020 %J Journal of Shandong University(Engineering Science) %R 10.6040/j.issn.1672-3961.0.2019.418 %P 17-26 %V 50 %N 2 %U {http://gxbwk.njournal.sdu.edu.cn/CN/abstract/article_1905.shtml} %8 2020-04-20 %X

In order to solve the problem that the existing deep learning based visual tracking algorithms paid attention to the deep features but neglected the shallow features, and the tracking network did not evaluate the tracking results, a visual tracking algorithm based on verifying network was proposed. The proposed algorithm consisted of tracking network and verifying network. In the tracking network, considering the fusion of deep features and shallow edge features, a multi-input residual network was designed to learn the relationship between the target and its corresponding Gaussian response map to obtain the position information of the target. In the verifying network, a shallow chain discriminate network was designed, and this paper compared the tracking results of tracking network and verifying network, and updated the tracking network according to the compared results. Therefore, the proposed algorithm not only took the deep features into account, but also avoided the loss of detail information. Furthermore, the tracking results were evaluated to prevent the continuation of error messages in the update. The experimental results illustrated that the proposed tracking algorithm achieved better tracking results than some other existing tracking methods.