论文标题
搜索和抢救空气载的光学截面
Search and Rescue with Airborne Optical Sectioning
论文作者
论文摘要
我们表明，在遮挡条件下自动检测可以通过在分类前结合多镜图像来显着改善。在这里，我们采用了机载光学截面（AOS）---一种合成孔径成像技术的图像集成，该技术使用相机无人机捕获非结构化的热光场---以96/93％的精确/召回率来实现此目的。在茂密的森林中发现失去或受伤的人通常在热记录中是不可行的，但是使用AOS积分图像可以实用。我们的发现为有效的未来搜索和救援技术奠定了基础，这些技术可以与自动驾驶或载人飞机结合使用。它们也可能对目前因部分遮障的人，动物或物体的分类不准确而受益。
We show that automated person detection under occlusion conditions can be significantly improved by combining multi-perspective images before classification. Here, we employed image integration by Airborne Optical Sectioning (AOS)---a synthetic aperture imaging technique that uses camera drones to capture unstructured thermal light fields---to achieve this with a precision/recall of 96/93%. Finding lost or injured people in dense forests is not generally feasible with thermal recordings, but becomes practical with use of AOS integral images. Our findings lay the foundation for effective future search and rescue technologies that can be applied in combination with autonomous or manned aircraft. They can also be beneficial for other fields that currently suffer from inaccurate classification of partially occluded people, animals, or objects.
