[Objectives]To overcome the challenges of tracking small targets in unmanned surface vehicle vision under the conditions of low feature resolution and similar environmental information,a multi-feature fu-sion-based continuous convolution operator tracking(MCCOT)algorithm is proposed.[Methods]The res-olution of multi-feature maps is enhanced using bicubic interpolation techniques to enable sub-pixel-level loc-alization.Efficiencies in target tracking are achieved through feature projection and sample space generation to mitigate filter overfitting.Furthermore,interference arising from similar environmental features on the filter is addressed by developing an update strategy for high-confidence models.[Results]As the experimental res-ults show,compared to traditional continuous convolution operator tracking algorithms,the proposed al-gorithm achieves an average success rate increase of 17.4%,average distance precision increase of 17.8%,and expected average overlap rate increase of 5.1%.[Conclusions]The proposed algorithm can deal with the problem of small target tracking confusion in marine environments,providing key technical support for im-proving the intelligent sensing capability of unmanned boats and marine robots.
关键词
海上小目标鲁棒跟踪/多特征融合/连续卷积算子/无人艇视觉
Key words
robust tracking of small targets at sea/multi-feature fusion/continuous convolutional operat-or/unmanned surface vehicle vision
维普
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