论文标题
视频萨尔中多目标跟踪的面向阴影的跟踪方法
Shadow-Oriented Tracking Method for Multi-Target Tracking in Video-SAR
论文作者
论文摘要
这项工作着重于视频合成孔径雷达中的多目标跟踪。具体来说，我们参考基于目标阴影的跟踪。当前方法的准确性有限，因为它们无法完全考虑阴影的特征和周围环境。阴影是低散射和变化的，导致缺失跟踪。周围环境可能会导致干扰，从而导致虚假跟踪。为了解决这些问题，我们提出了一种面向阴影的多目标跟踪方法（SOTRACK）。为避免错误的跟踪，提出了预处理模块以增强周围环境的阴影，从而减少了它们的干扰。为避免错过的跟踪，基于深度学习的检测方法旨在彻底学习阴影的特征，从而增加准确的估计。此外，召回模块旨在召回错过的阴影。我们对测量数据进行实验。结果表明，与其他方法相比，Sotrack在跟踪准确性18.4％方面的性能要高得多。消融研究证实了所提出的模块的有效性。
This work focuses on multi-target tracking in Video synthetic aperture radar. Specifically, we refer to tracking based on targets' shadows. Current methods have limited accuracy as they fail to consider shadows' characteristics and surroundings fully. Shades are low-scattering and varied, resulting in missed tracking. Surroundings can cause interferences, resulting in false tracking. To solve these, we propose a shadow-oriented multi-target tracking method (SOTrack). To avoid false tracking, a pre-processing module is proposed to enhance shadows from surroundings, thus reducing their interferences. To avoid missed tracking, a detection method based on deep learning is designed to thoroughly learn shadows' features, thus increasing the accurate estimation. And further, a recall module is designed to recall missed shadows. We conduct experiments on measured data. Results demonstrate that, compared with other methods, SOTrack achieves much higher performance in tracking accuracy-18.4%. And ablation study confirms the effectiveness of the proposed modules.
