标题： 内河航行船舶的视觉轨迹预测 显示英文标题

标题： Visual Trajectory Prediction of Vessels for Inland Navigation

摘要： 内河航行的未来越来越多地依赖于自主系统和远程操作，强调了对准确船舶轨迹预测的需求。 本研究通过整合先进的目标检测方法、卡尔曼滤波器和基于样条的插值，解决了基于视频的船舶跟踪和预测的挑战。 然而，现有的检测系统由于复杂的周围环境，经常在内河水道中误分类物体。 包括BoT-SORT、Deep OC-SORT和ByeTrack在内的跟踪算法的比较评估突出了卡尔曼滤波器在提供平滑轨迹方面的鲁棒性。 来自不同场景的实验结果表明，在预测船舶运动方面具有更高的准确性，这对于避免碰撞和态势感知至关重要。 研究结果强调了为内河航行定制数据集和模型的必要性。 未来的工作将扩展数据集并结合船舶分类以改进预测，从而支持复杂环境中的自主系统和人工操作员。 显示英文摘要

摘要： 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.