Visual Trajectory Prediction of Vessels for Inland Navigation

TL;DR

This paper presents an integrated approach combining object detection, Kalman filtering, and spline interpolation to improve vessel trajectory prediction in inland waterways, crucial for autonomous navigation and collision avoidance.

Contribution

It introduces a comparative evaluation of tracking algorithms and demonstrates the effectiveness of Kalman filters for robust vessel trajectory prediction in complex inland environments.

Findings

Kalman filter provides smoother and more accurate vessel trajectories.

Deep OC-SORT and BoT-SORT outperform other tracking algorithms in inland scenarios.

Experimental results show significant improvement in prediction accuracy across diverse conditions.

Abstract

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.