PDF(Pubmed)
Abstract：
Through our respiratory system, many viruses and diseases frequently spread and pass from one person to another. Covid-19 served as an example of how crucial it is to track down and cut back on contacts to stop its spread. There is a clear gap in finding automatic methods that can detect hand-to-face contact in complex urban scenes or indoors. In this paper, we introduce a computer vision framework, called FaceTouch, based on deep learning. It comprises deep sub-models to detect humans and analyse their actions. FaceTouch seeks to detect hand-to-face touches in the wild, such as through video chats, bus footage, or CCTV feeds. Despite partial occlusion of faces, the introduced system learns to detect face touches from the RGB representation of a given scene by utilising the representation of the body gestures such as arm movement. This has been demonstrated to be useful in complex urban scenarios beyond simply identifying hand movement and its closeness to faces. Relying on Supervised Contrastive Learning, the introduced model is trained on our collected dataset, given the absence of other benchmark datasets. The framework shows a strong validation in unseen datasets which opens the door for potential deployment.
摘要：
通过我们的呼吸系统，许多病毒和疾病经常从一个人传播到另一个人。Covid-19就是一个例子，说明追踪和减少接触以阻止其传播是多么重要。在寻找能够在复杂的城市场景或室内检测到面对面接触的自动方法方面存在明显差距。在本文中,我们介绍了一个计算机视觉框架，叫做FaceTouch,基于深度学习。它包括深度子模型来检测人类并分析他们的行为。FaceTouch试图检测野外的面对面触摸，例如通过视频聊天，巴士镜头,或闭路电视供稿。尽管面部部分遮挡，引入的系统通过利用诸如手臂运动的身体姿势的表示来学习从给定场景的RGB表示检测面部触摸。这已被证明在复杂的城市场景中很有用，除了简单地识别手部运动及其与面部的亲密关系。依靠监督对比学习，引入的模型是在我们收集的数据集上训练的，鉴于缺乏其他基准数据集。该框架在看不见的数据集中显示了强大的验证，为潜在的部署打开了大门。
