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• W2947789712 abstract "Abstract In the trackers based on correlation filter, the limitation of tracking samples and the efficiency of the solution affect the performance of the trackers. Based on the Discriminative Scale Space Tracking(DSST), we proposed a new framework to improve correlation filter tracking via context fusion and subspace constraint. First, the target and background are jointly modeled to enhance the ability to distinguish between target and background. Second, to achieve higher robustness, subspace constraint is applied to DSST, and it can be solved efficiently by subspace-based alternating direction method of multipliers(SADMM). Finally, through the model update strategy, high-quality samples are selected to update. The proposed algorithm is validated on the OTB2013, OTB2015 and TC128. Experimental results show that compared with fast DSST(fDSST), success rate and accuracy of the proposed method are increased by 4.2% and 6.8% respectively. Compared with the state-of-the-art trackers, the proposed method achieves a competitive result with real-time speed." @default.
• W2947789712 created "2019-06-07" @default.
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• W2947789712 date "2019-07-01" @default.
• W2947789712 modified "2023-10-05" @default.
• W2947789712 title "Robust correlation filter tracking via context fusion and subspace constraint" @default.
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• W2947789712 doi "https://doi.org/10.1016/j.jvcir.2019.05.014" @default.
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