多特征融合的无人艇视觉小目标鲁棒跟踪

Multi-feature fusion-based robust tracking of small targets in unmanned surface vehicle vision

    摘要
  • 摘要:
    目的 针对低特征分辨率、相似环境信息引起的无人艇视觉小目标跟踪混淆问题，提出一种多特征融合的连续卷积算子跟踪算法。
    方法 首先，采用双三次插值技术，提高多特征图分辨率，实现亚像素级定位；其次，利用特征投影和生成样本空间，提高目标跟踪的效率，避免滤波器过拟合；最后，设计高置信度模型更新策略，解决相似环境信息对滤波器的干扰问题。
    结果 结果表明：相较于传统的连续卷积算子跟踪算法，平均成功率提升17.4%，平均距离精度指标提升17.8%，期望平均覆盖率提升5.1%。
    结论 该算法法能够处理海洋环境下的小目标跟踪混淆问题，为提升无人艇及海洋机器人的智能感知能力，提供关键技术支撑。

     

    Abstract:
    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 fusion-based continuous convolution operator tracking （MCCOT） algorithm is proposed.
    Methods  The resolution of multi-feature maps is enhanced using bicubic interpolation techniques to enable sub-pixel-level localization. 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 results show, compared to traditional continuous convolution operator tracking algorithms, the proposed algorithm 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 improving the intelligent sensing capability of unmanned boats and marine robots.

     

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