论文标题
感知人类：从单眼3D本地化到社会疏远
Perceiving Humans: from Monocular 3D Localization to Social Distancing
论文作者
论文摘要
在智能运输系统（ITS）的背景下，感知人类通常依赖于多个相机或昂贵的LiDAR传感器。在这项工作中，我们提出了一种新的基于具有成本效益的视觉方法，该方法可感知人类在3D中的位置及其从单个图像中的身体取向。我们通过提出与点估计相比预测置信区间的神经网络体系结构，以解决与不良单程3D任务相关的挑战。我们的神经网络以一种不确定性度量估算人类3D身体位置及其方向。我们提出的解决方案（i）是隐私安全，（ii）与任何固定或移动的摄像机一起使用，（iii）不依赖地面平面估计。我们证明了我们在三个应用方面的方法的性能：在3D中定位人类，检测社交互动，并验证由于19号疫情而引起的最近安全措施的遵守情况。我们表明，与简单的基于位置的规则相反，有可能重新考虑“社会疏远”的概念作为社会互动的一种形式。我们将公开分享源代码的开放科学任务。
Perceiving humans in the context of Intelligent Transportation Systems (ITS) often relies on multiple cameras or expensive LiDAR sensors. In this work, we present a new cost-effective vision-based method that perceives humans' locations in 3D and their body orientation from a single image. We address the challenges related to the ill-posed monocular 3D tasks by proposing a neural network architecture that predicts confidence intervals in contrast to point estimates. Our neural network estimates human 3D body locations and their orientation with a measure of uncertainty. Our proposed solution (i) is privacy-safe, (ii) works with any fixed or moving cameras, and (iii) does not rely on ground plane estimation. We demonstrate the performance of our method with respect to three applications: locating humans in 3D, detecting social interactions, and verifying the compliance of recent safety measures due to the COVID-19 outbreak. We show that it is possible to rethink the concept of "social distancing" as a form of social interaction in contrast to a simple location-based rule. We publicly share the source code towards an open science mission.
