This article investigates a class of double phase problem with L1 data. Some new criteria to guarantee that the existence and uniqueness of non-negative renormalized solutions for the considered problem are established by using the approximation and energy methods.
The aim of this paper is to establish the multiplicity of solutions for double‐phase problem. Employing the Nehari manifold approach, we show that the problem has at least two nontrivial solutions.
ABSTRACT.Aiming at the problem of occlusion in object tracking, adaptive feature fusion object tracking with kernelized correlation filters is proposed to advance the robustness of the correlation filter for object tracking. Firstly, feature fusion based on tracking confidence is proposed to improve the robustness of object tracking. Then combined with the detection method of tracking confidence to solve the object occlusion and tracking failure. Finally, template updating strategy based on tracking confidence is proposed to solve the drift problem. The experiment carries on the OTB100 dataset, and compares with other tracking algorithms. The results show that the tracking precision is 4.5% higher than that of the optimal algorithm, and the tracking success rate is improved by 6.1%. Moreover, the object can be well tracked under the occlusion.
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