针对海上复杂环境，提出高效并行多目标追踪框架，提升无人船感知能力。
DMSORT: An efficient parallel maritime multi-object tracking architecture for unmanned vessel platforms
- 双分支并行架构，融合检测、重识别与动态相机运动补偿
- 在新加坡海事数据集上达到顶尖性能，运行速度最快且抗遮挡能力强
- 适合无人船等资源受限平台，兼顾实时性与追踪精度
通过鲁棒的多目标追踪（MOT）实现精准海洋环境感知，对保障船舶航行安全与有效海上监视至关重要。然而，复杂的海上环境常引发相机运动及视觉退化，给追踪带来挑战。为此，本文提出一种高效的双分支海上SORT（DMSORT）方法。核心为带有仿射补偿的并行追踪器，包含目标检测与重识别（ReID）分支，以及专门用于动态相机运动估计的分支。检测模块引入可逆列式检测网络（RCDN），利用多层次视觉特征实现鲁棒检测；设计轻量级Transformer外观提取器（Li-TAE），捕捉全局上下文信息并生成稳定外观特征。另一分支通过构建投影变换，分离平台自身运动与目标内在运动，在卡尔曼滤波中实施平台运动补偿，从而稳定真实目标轨迹。最后，聚类优化的特征融合模块有效结合运动与外观线索，在噪声、遮挡和漂移下保持身份一致性。在新加坡海事数据集上的大量评估表明，DMSORT达到当前最优性能，是现有基于ReID的MOT框架中运行速度最快者，同时保持高身份一致性和对抖动、遮挡的鲁棒性。代码已公开：https://github.com/BiscuitsLzy/DMSORT-An-efficient-parallel-maritime-multi-object-tracking-architecture-。
原文摘要 · Abstract (English)
Accurate perception of the marine environment through robust multi-object tracking (MOT) is essential for ensuring safe vessel navigation and effective maritime surveillance. However, the complicated maritime environment often causes camera motion and subsequent visual degradation, posing significant challenges to MOT. To address this challenge, we propose an efficient Dual-branch Maritime SORT (DMSORT) method for maritime MOT. The core of the framework is a parallel tracker with affine compensation, which incorporates an object detection and re-identification (ReID) branch, along with a dedicated branch for dynamic camera motion estimation. Specifically, a Reversible Columnar Detection Network (RCDN) is integrated into the detection module to leverage multi-level visual features for robust object detection. Furthermore, a lightweight Transformer-based appearance extractor (Li-TAE) is designed to capture global contextual information and generate robust appearance features. Another branch decouples platform-induced and target-intrinsic motion by constructing a projective transformation, applying platform-motion compensation within the Kalman filter, and thereby stabilizing true object trajectories. Finally, a clustering-optimized feature fusion module effectively combines motion and appearance cues to ensure identity consistency under noise, occlusion, and drift. Extensive evaluations on the Singapore Maritime Dataset demonstrate that DMSORT achieves state-of-the-art performance. Notably, DMSORT attains the fastest runtime among existing ReID-based MOT frameworks while maintaining high identity consistency and robustness to jitter and occlusion. Code is available at: https://github.com/BiscuitsLzy/DMSORT-An-efficient-parallel-maritime-multi-object-tracking-architecture-.
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