论文标题
视觉社会距离的单一图像人类近端估计
Single Image Human Proxemics Estimation for Visual Social Distancing
论文作者
论文摘要
在这项工作中，我们解决了估计所谓的“社会疏远”的问题，因为在不受约束的情况下有一个未校准的图像。我们的方法提出了一种半自动解决方案，以近似场景地面和图像平面之间的同构矩阵。借助估计的同构象，我们利用现成的姿势探测器在图像上检测身体姿势，并使用其身体零件的长度来理解其人际关系距离。进一步检查了个人间距离，以检测可能违反社会距离规则的行为。我们对公共领域数据集上的基线验证了我们提出的方法，我们在人际关系间为其提供了地面图。此外，我们证明了在实际测试方案中部署的方法的应用，目前使用有关人际关系距离的统计数据来改善关键环境中的安全性。
In this work, we address the problem of estimating the so-called "Social Distancing" given a single uncalibrated image in unconstrained scenarios. Our approach proposes a semi-automatic solution to approximate the homography matrix between the scene ground and image plane. With the estimated homography, we then leverage an off-the-shelf pose detector to detect body poses on the image and to reason upon their inter-personal distances using the length of their body-parts. Inter-personal distances are further locally inspected to detect possible violations of the social distancing rules. We validate our proposed method quantitatively and qualitatively against baselines on public domain datasets for which we provided groundtruth on inter-personal distances. Besides, we demonstrate the application of our method deployed in a real testing scenario where statistics on the inter-personal distances are currently used to improve the safety in a critical environment.
