Non-line-of-sight (NLOS) tracking has drawn increasing attention in recent years, due to its ability to detect object motion out of sight. Most previous works on NLOS tracking rely on active illumination, e.g., laser, and suffer from high cost and elaborate experimental conditions. Besides, these techniques are still far from practical application due to oversimplified settings. In contrast, we propose a purely passive method to track a person walking in an invisible room by only observing a relay wall, which is more in line with real application scenarios, e.g., security. To excavate imperceptible changes in videos of the relay wall, we introduce difference frames as an essential carrier of temporal-local motion messages. In addition, we propose PAC-Net, which consists of alternating propagation and calibration, making it capable of leveraging both dynamic and static messages on a frame-level granularity. To evaluate the proposed method, we build and publish the first dynamic passive NLOS tracking dataset, NLOS-Track, which fills the vacuum of realistic NLOS datasets. NLOS-Track contains thousands of NLOS video clips and corresponding trajectories. Both real-shot and synthetic data are included.

翻译：非直视（NLOS）跟踪近年来越来越受到关注，因为它能够检测不在视线范围内的物体运动。以往大多数针对NLOS跟踪的研究依赖于主动照明，如激光，且成本高昂，实验条件严苛。此外，这些技术由于过于简化，仍然远离实际应用。相反，我们提出了一种纯被动的方法，仅通过观察中继墙来跟踪在无形房间内行走的人，这更符合实际应用场景，例如安保。为挖掘中继墙视频中难以察觉的变化，我们引入差分帧作为时空局部运动信息的必要载体。此外，我们提出PAC-Net，它由交替的传播和校准组成，使其能够在帧级粒度上利用动态和静态信息。为了评估所提出的方法，我们构建并发布了第一个动态无源NLOS跟踪数据集NLOS-Track，填补了现实NLOS数据集的真空。NLOS-Track包含数千个NLOS视频片段和相应的轨迹。其中包括实际拍摄和合成数据。