基于视觉的内河船舶轨迹预测，提升航行安全与自主决策能力
Visual Trajectory Prediction of Vessels for Inland Navigation
- 融合检测、卡尔曼滤波与样条插值，提升轨迹平滑性
- 在复杂水道场景中实现更精准的船舶运动预测，支持防碰撞
- 适合内河自动驾驶与远程操控系统开发人员参考
内河航行日益依赖自主系统和远程操作，准确预测船舶轨迹至关重要。本文通过整合先进的目标检测方法、卡尔曼滤波器与样条插值，解决基于视频的船舶追踪与预测难题。现有检测系统在复杂水域常出现误分类。对BoT-SORT、Deep OC-SORT与ByeTrack等跟踪算法的对比评估表明，卡尔曼滤波器在生成平滑轨迹方面表现更优。多场景实验验证了预测精度的提升，这对碰撞避免与态势感知具有重要意义。研究强调需为内河航行定制数据集与模型。未来工作将扩充数据集并引入船舶分类，以进一步优化预测，支持自主系统与人工操作员在复杂环境中的应用。
原文摘要 · Abstract (English)
The future of inland navigation increasingly relies on autonomous systems and remote operations, emphasizing the need for accurate vessel trajectory prediction. This study addresses the challenges of video-based vessel tracking and prediction by integrating advanced object detection methods, Kalman filters, and spline-based interpolation. However, existing detection systems often misclassify objects in inland waterways due to complex surroundings. A comparative evaluation of tracking algorithms, including BoT-SORT, Deep OC-SORT, and ByeTrack, highlights the robustness of the Kalman filter in providing smoothed trajectories. Experimental results from diverse scenarios demonstrate improved accuracy in predicting vessel movements, which is essential for collision avoidance and situational awareness. The findings underline the necessity of customized datasets and models for inland navigation. Future work will expand the datasets and incorporate vessel classification to refine predictions, supporting both autonomous systems and human operators in complex environments.
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