RadarTracker: An End-to-End Transformer Framework for Vessel Tracking Using X-Band Marine Radar

Le résumé fourni par la source

In dense maritime environments with complex shipping routes, small-scale vessel radar imaging, frequent occlusions, and severe sea clutter present catastrophic challenges for maritime radar-based multi-vessel tracking. Precisely modeling vessel targets under such interference and achieving robust tracking through occluded routes is therefore critical for maritime surveillance tasks. To this end, we propose RadarTracker, a novel end-to-end temporal Transformer framework for X-band marine radar multi-vessel tracking. RadarTracker consists of a CNN-based feature extractor, a density enhancement encoder, and an occlusion-aware dynamic decoder. Specifically, the encoder is built upon a Deformable Transformer backbone, integrated with a density-aware module and density feature enhancement to address the low signal-to-noise ratio and densely distributed vessel targets in radar imagery. Subsequently, to achieve accurate trajectory regression under varying traffic densities and complex occlusions along shipping routes, we design an occlusion-aware decoder with a dynamic query allocation mechanism that enables seamless target initialization, identity preservation, and short-term re-identification, effectively balancing tracking accuracy and efficiency under diverse traffic conditions. Finally, we construct a high-density shipping lane dataset collected along the Yangtze River in China to validate the proposed approach. Experimental results demonstrate that RadarTracker achieves a consistent 5% performance improvement over state-of-the-art baselines across varying traffic densities, while maintaining deployment-friendly computational efficiency. The implementation of our method will be publicly available at https://github.com/wangzihanggg/RadarTracker.

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