王宁, 吴伟, 王元元, 等. 多特征融合的无人艇视觉目标长时相关鲁棒跟踪[J]. 中国舰船研究, 2024, 19(1): 62–74. doi: 10.19693/j.issn.1673-3185.03364
引用本文: 王宁, 吴伟, 王元元, 等. 多特征融合的无人艇视觉目标长时相关鲁棒跟踪[J]. 中国舰船研究, 2024, 19(1): 62–74. doi: 10.19693/j.issn.1673-3185.03364
shu
WANG N, WU W, WANG Y Y, et al. Long-term correlation robust tracking of visual targets for unmanned surface vehicles using multi-feature fusion[J]. Chinese Journal of Ship Research, 2024, 19(1): 62–74 (in both Chinese and English). doi: 10.19693/j.issn.1673-3185.03364
Citation: WANG N, WU W, WANG Y Y, et al. Long-term correlation robust tracking of visual targets for unmanned surface vehicles using multi-feature fusion[J]. Chinese Journal of Ship Research, 2024, 19(1): 62–74 (in both Chinese and English). doi: 10.19693/j.issn.1673-3185.03364
shu

多特征融合的无人艇视觉目标长时相关鲁棒跟踪

Long-term correlation robust tracking of visual targets for unmanned surface vehicles using multi-feature fusion

    摘要
  • 摘要:
    目的 针对显著海浪遮挡、相机剧烈晃动引起的无人艇视觉目标跟踪脱靶问题，提出一种基于多特征融合的长时相关鲁棒跟踪算法。
    方法 首先，采用多特征融合技术，增强目标特征表达，提高目标模型鲁棒性；其次，利用高维特征降维和响应图子网格插值，提高目标跟踪的效率与精度；然后，设计水面目标重识别机制，解决目标完全脱离视野时的稳定跟踪问题；最后，采用多个代表性视频数据集进行验证和比较分析。
    结果 实验结果表明，相较于传统的长时相关跟踪算法，平均成功率提升15.7%，平均距离精度指标提升30.3%，F-Score指标提升7.0%。
    结论 所提算法能够处理恶劣海况下的目标脱靶问题，对于提升无人船艇及海洋机器人智能感知能力，具有重要技术支撑意义。

     

    Abstract:
    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 proposed 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 average 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 perception capability of USVs and ocean robots.

     

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