Abstract
In order to solve the challenges of In-plane/Out-of-plane Rotation (IPR/OPR), fast motion (FM) and occlusion (OCC), a new robust visual tracking framework combining an adaptive template update strategy and tracking validity evaluation, named (AU_DKCF) is presented in this paper. Specifically, the proposed appearance discriminant models are firstly used to determine the tracking validity, and then a new adaptive template update strategy is introduced, which provides an efficient update mechanism to distinguish IPR/OPR from FM and OCC states, and furthermore, a new visual tracking framework AU_DKCF is presented, which combines object detection to distinct FM and OCC states. We implement two versions of the proposed tracker with the representations from both conventional hand-crafted and deep convolution neural networks (CNNs) based features to validate the strong compatibility of the algorithm. Experiment results demonstrate the state-of-the-art performance in tracking accuracy and speed for processing the cases of IPR/OPR, FM and OCC.
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This work was supported by the National Nature Science Foundation of China (No. 61572458).
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Ning, X., Li, W., Tian, W., Xuchi, Dongxiaoli, Zhangliping (2018). Deep Adaptive Update of Discriminant KCF for Visual Tracking. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11306. Springer, Cham. https://doi.org/10.1007/978-3-030-04224-0_38
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DOI: https://doi.org/10.1007/978-3-030-04224-0_38
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