疫情期间广域社交距离监测：一种密度图与分割方法
Monitoring Social-distance in Wide Areas during Pandemics: a Density Map and Segmentation Approach
AI总结：
本文提出一种基于端到端深度学习的广域社交距离监测框架，通过密度图和分割两种方法检测违反社交距离的人群区域，并在PET2009和CityStreet数据集上验证其在遮挡和远距离场景下的有效性。
摘要：
随着全球范围内防控措施的放松，在拥挤的公共场所监测社交距离对于防止新一波大规模COVID-19感染至关重要。该领域近期的工作局限于在走廊等场景中检测小规模人群的社交距离，其方法是对图像中每个人的全身进行单独检测。在本工作中，我们提出了一种利用端到端深度学习监测社交距离的新框架，用于在可能存在严重遮挡的广域场景中检测违反社交距离的人群。我们的框架包括基于真值密度图构建新的真值标注，并提出两种不同的解决方案：一种基于密度图，另一种基于分割，用于检测违反社交距离约束的人群。我们使用由PET2009和CityStreet数据集生成的真值标注来评估两种方法的结果。我们表明，即使在人群被严重遮挡或距离摄像机较远的情况下，该框架也能很好地提供未遵守社交距离的区域。
英文摘要：
With the relaxation of the containment measurements around the globe, monitoring the social distancing in crowded public places is of grate importance to prevent a new massive wave of COVID-19 infections. Recent works in that matter have limited themselves by detecting social distancing in corridors up to small crowds by detecting each person individually considering the full body in the image. In this work, we propose a new framework for monitoring the social-distance using end-to-end Deep Learning, to detect crowds violating the social-distance in wide areas where important occlusions may be present. Our framework consists in the creation of a new ground truth based on the ground truth density maps and the proposal of two different solutions, a density-map-based and a segmentation-based, to detect the crowds violating the social-distance constrain. We assess the results of both approaches by using the generated ground truth from the PET2009 and CityStreet datasets. We show that our framework performs well at providing the zones where people are not following the social-distance even when heavily occluded or far away from one camera.
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