TrajE2E-MOT: Trajectory-Aware End-to-End Multi-Object Tracking in Maritime Radar

For autonomous maritime perception and situational awareness, the end-to-end multi-object tracking paradigm has achieved complete learning, from image sequences to tracking results, reducing the reliance on manually designed association rules and holding great potential. However, in maritime radar video multi-object tracking, due to the limited visual features of targets and significant feature variations under long-term tracking, problems such as identity switching are prone to occur, making it difficult to directly apply existing end-to-end approaches. To solve these problems, this paper proposes a trajectory-aware end-to-end multi-object tracking method. The real-time trajectory of the targets contains temporal context information. This work uses it as prior knowledge to enhance visual feature encoding and compensate for the shortcomings of single-frame visual features. Specifically, the trajectory feature is encoded by the trajectory encoder module while, simultaneously, the visual features are encoded through the backbone and the visual feature encoder module. Then, in the frame-trajectory cross-modal attention module, the trajectory feature encoding is used to reconstruct the visual feature encoding with cross-attention, dynamically enhancing the features related to the target identity. Experiments on actual collected maritime radar video data show that the proposed method is effective, achieving improvements in several key indicators.