摘要： 传统视觉感知方法仅能捕获视线范围内的物体信息，无法检测被场景障碍物遮蔽的物体。非视域（NLOS）方法则通过分析反射或投射到可见中继表面的光或电磁信号来还原这些被遮挡物体的信息。然而，经过多年研究，现有NLOS方法在户外环境中捕捉经多次反射的微弱信号成分仍是巨大挑战，这为复杂动态的户外真实场景的非视域感知应用带来了巨大挑战。为此，本文提出采用高性价比的毫米波雷达实现大规模动态场景中隐藏目标的检测与追踪。此类雷达已在汽车工业中广泛应用并支持低成本大规模生产。在将雷达点云转换为伪图像后，我们用所提出的双阶段注意力网络（TSAN）进行隐藏目标的检测与追踪。实验表明，TSAN双阶段注意力网络模型在多种交并比阈值下显著提升多类别检测性能，平均精度均值（mAP）达75.62%。相较现有成果，TSAN网络的mAP提升5.99%，性能优于当前最先进方法。此外，基于本文方法构建的原型样机，展现了一种低成本非视域目标检测与跟踪系统方案。实验验证了该系统能够实时、经济高效地实现NLOS目标检测与跟踪任务。

Abstract: Traditional visual perception methods can only capture information about objects within the line of sight and are unable to detect objects obscured by obstacles in the scene. Non-line-of-sight (NLOS) methods, on the other hand, reconstruct information about these occluded objects by analyzing light or electromagnetic signals reflected or projected onto visible relay surfaces. However, after years of research, existing NLOS methods still face a significant challenge in capturing faint signal components that have undergone multiple reflections in outdoor environments. This poses considerable challenges for non-line-of-sight (NLOS) perception applications in complex, dynamic outdoor real-world scenarios. To address this, this paper proposes the use of cost-effective millimeter-wave radar to detect and track hidden targets in large-scale dynamic scenes. Such radar has been widely adopted in the automotive industry and supports low-cost mass production. After converting radar point clouds into pseudo-images, we apply the proposed two-stage attention network (TSAN) for the detection and tracking of hidden targets. Experiments show that the TSAN network model significantly improves detection performance across multiple categories under various Intersection over Union (IoU) thresholds, achieving a mean average precision (mAP) of 75.62%. Compared with existing results, the TSAN network yields a 5.99% improvement in mAP, outperforming current state-of-the-art methods. In addition, the prototype built based on the method described in this paper provides a low-cost solution for NLOS target detection and tracking systems, and verifies its effectiveness in achieving cost-effective real-time NLOS target detection and tracking.