Passive Non-line-of-sight Imaging for Moving Targets with an Event Camera
This addresses the limitation of existing passive NLOS imaging methods in recognizing moving targets, which is incremental but domain-specific.
The paper tackles the problem of non-line-of-sight imaging for moving targets by proposing a novel event-based passive method, achieving a 20% improvement in PSNR and 10% in LPIPS compared to frame-based methods while using only 2% of the data volume.
Non-line-of-sight (NLOS) imaging is an emerging technique for detecting objects behind obstacles or around corners. Recent studies on passive NLOS mainly focus on steady-state measurement and reconstruction methods, which show limitations in recognition of moving targets. To the best of our knowledge, we propose a novel event-based passive NLOS imaging method. We acquire asynchronous event-based data which contains detailed dynamic information of the NLOS target, and efficiently ease the degradation of speckle caused by movement. Besides, we create the first event-based NLOS imaging dataset, NLOS-ES, and the event-based feature is extracted by time-surface representation. We compare the reconstructions through event-based data with frame-based data. The event-based method performs well on PSNR and LPIPS, which is 20% and 10% better than frame-based method, while the data volume takes only 2% of traditional method.