What You Can Learn by Staring at a Blank Wall
What You Can Learn by Staring at a Blank Wall
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DOI：
10.1109/iccv48922.2021.00233
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发表时间：
2021-08
期刊：
影响因子：
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通讯作者：
Prafull Sharma;M. Aittala;Y. Schechner;A. Torralba;G. Wornell;W. Freeman;F. Durand
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作者：
Prafull Sharma;M. Aittala;Y. Schechner;A. Torralba;G. Wornell;W. Freeman;F. Durand
我们提出了一种被动非视线方法，它通过观察未知房间中的空白墙壁来推断人或人的活动数量。我们的技术分析了墙的视频中间接照明的复杂而不可察觉的变化，以揭示与场景隐藏部分的运动相关的信号。我们使用这个信号来区分零个、一个或两个移动的人，或者一个人在隐藏场景中的活动。我们使用从20个不同场景收集的数据训练两个卷积神经网络，在不可见的测试环境和实时在线设置下，两个任务的准确率都达到了≈94%。与其他被动非视距方法不同，该技术不依赖于已知的遮光器或可控光源，并且无需重新校准即可推广到未知房间。我们用真实数据和合成数据分析了该方法的泛化能力和鲁棒性，并研究了场景参数对信号质量的影响。
We present a passive non-line-of-sight method that infers the number of people or activity of a person from the observation of a blank wall in an unknown room. Our technique analyzes complex imperceptible changes in indirect illumination in a video of the wall to reveal a signal that is correlated with motion in the hidden part of a scene. We use this signal to classify between zero, one, or two moving people, or the activity of a person in the hidden scene. We train two convolutional neural networks using data collected from 20 different scenes, and achieve an accuracy of ≈ 94% for both tasks in unseen test environments and real-time online settings. Unlike other passive non-line-of-sight methods, the technique does not rely on known occluders or controllable light sources, and generalizes to unknown rooms with no recalibration. We analyze the generalization and robustness of our method with both real and synthetic data, and study the effect of the scene parameters on the signal quality.1