解决视频合成孔径雷达中多目标跟踪的模糊与漂移问题
Multiple Object Tracking in Video SAR: A Benchmark and Tracking Baseline
- 引入线特征增强机制，利用运动阴影减少误报
- 提出运动感知线索丢弃策略，提升目标外观变化下的追踪稳定性
- 构建首个公开视频SAR多目标跟踪基准数据集
在视频合成孔径雷达（Video SAR）多目标跟踪任务中，目标运动引起的多普勒偏移会生成易被误判为静态遮挡阴影的伪影，同时多普勒失配导致的目标外观变化会造成关联失败并破坏轨迹连续性。当前该领域缺乏公开基准数据集，制约了算法评估与比较。为此，本文收集并标注了45个含运动目标的视频SAR序列，构建了首个公开的视频SAR多目标跟踪基准数据集（VSMB）。针对运动目标的拖尾和模糊问题，提出线特征增强机制，突出运动阴影的积极作用，降低静态遮挡引起的误报；针对目标外观变化问题，设计运动感知线索丢弃机制，显著提升跟踪鲁棒性。所提模型在VSMB上达到当前最优性能，相关数据集与代码已开源至https://github.com/softwarePupil/VSMB。
原文摘要 · Abstract (English)
In the context of multi-object tracking using video synthetic aperture radar (Video SAR), Doppler shifts induced by target motion result in artifacts that are easily mistaken for shadows caused by static occlusions. Moreover, appearance changes of the target caused by Doppler mismatch may lead to association failures and disrupt trajectory continuity. A major limitation in this field is the lack of public benchmark datasets for standardized algorithm evaluation. To address the above challenges, we collected and annotated 45 video SAR sequences containing moving targets, and named the Video SAR MOT Benchmark (VSMB). Specifically, to mitigate the effects of trailing and defocusing in moving targets, we introduce a line feature enhancement mechanism that emphasizes the positive role of motion shadows and reduces false alarms induced by static occlusions. In addition, to mitigate the adverse effects of target appearance variations, we propose a motion-aware clue discarding mechanism that substantially improves tracking robustness in Video SAR. The proposed model achieves state-of-the-art performance on the VSMB, and the dataset and model are released at https://github.com/softwarePupil/VSMB.
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