[Objective]To address the problem of visual target tracking failure caused by significant wave interference and severe camera shaking in unmanned surface vehicles(USVs),a multi-feature fusion long-term correlation robust tracking algorithm is proposed.[Methods]First,the multi-feature fusion technique is used to enhance the expression of target features and improve the robustness of the target model.Then,high-dimensional feature dimension reduction and response map sub-grid interpolation are utilized to improve the efficiency and accuracy of target tracking.After that,a mechanism for water surface target re-identification is designed to address the issue of stable tracking when the target is completely out of sight.Finally,the pro-posed algorithm is validated and compared through multiple representative video datasets.[Results]The experimental results show that compared with traditional long-term correlation tracking algorithms,the aver-age success rate is improved by 15.7%,the average distance precision index is improved by 30.3%and the F-score index is improved by 7.0%.[Conclusion]The proposed algorithm can handle target tracking failure in harsh marine environments and has important technical support significance for improving the intelligent per-ception capability of USVs and ocean robots.
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