[2209.13300] Passive Non-line-of-sight Imaging for Moving Targets with an Event Camera
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Computer Science > Computer Vision and Pattern Recognition

arXiv:2209.13300 (cs)
[Submitted on 27 Sep 2022]

Title:Passive Non-line-of-sight Imaging for Moving Targets with an Event Camera

Authors:Conghe Wang (1), Yutong He (2), Xia Wang (1), Honghao Huang (2), Changda Yan (1), Xin Zhang (1), Hongwei Chen (2) ((1) Key Laboratory of Photoelectronic Imaging Technology and System of Ministry of Education of China, School of Optics and Photonics, Beijing Institute of Technology (2) Beijing National Research Center for Information Science and Technology (BNRist), Department of Electronic Engineering, Tsinghua University)
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Abstract: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.
Subjects: Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV); Optics (physics.optics)
Cite as: arXiv:2209.13300 [cs.CV]
  (or arXiv:2209.13300v1 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2209.13300
Journal reference: [J]. Chinese Optics Letters, 2023, 21(6): 061103
Related DOI: https://doi.org/10.3788/COL202321.061103

Submission history

From: Conghe Wang [view email]
[v1] Tue, 27 Sep 2022 10:56:14 UTC (1,643 KB)
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