Detection based visual tracking with convolutional neural network

In this paper, we propose a detection strategy based visual object tracking algorithm. We employ multiple trackers using layers of deep convolutional neural network (CNN) features. Each tracker which is correlation filter based tracking framework tracks an object forwardly and then backwardly. By analyzing the forward and backward trajectories, we measure the robustness of tracking results. A detection strategy which is based on locally adaptive regression kernels (LARK) feature is carried out according to the robustness of tracking result. Target can be located from the provided candidates. Extensive experimental results show that the proposed method improves the accuracy and robustness of tracking, achieving state-of-the-art results on several recent benchmark datasets. (C) 2019 Elsevier B.V. All rights reserved.