论文标题
通过实时视频处理边缘的实时视频处理较小的隐私保护
Minor Privacy Protection Through Real-time Video Processing at the Edge
论文作者
论文摘要
闭路电视（CCTV）摄像机收集了许多有关个人的个人信息，包括家庭的次要成员，这引起了很多隐私问题。特别是，揭示儿童的身份或活动可能会损害他们的幸福感。在本文中，我们研究了边缘监视系统负担得起的轻量级解决方案，这是可行和准确的，以确定未成年人，以便可以采用适当的隐私措施。最先进的深度学习体系结构以级联的方式进行了修改和重新使用，以最大程度地提高模型的准确性。管道从输入框架中提取面孔，并将每个框架分类为成人或孩子。超过20,000个标记的样品点用于分类。我们探讨了该模型在网络边缘的边缘模具体系结构中使用所需的时间和资源，在那里我们可以在CPU上实现接近实时的性能。定量实验结果表明，与其他基于面部识别的儿童检测方法相比，分类的精度为92.1％，其精度为92.1％。
The collection of a lot of personal information about individuals, including the minor members of a family, by closed-circuit television (CCTV) cameras creates a lot of privacy concerns. Particularly, revealing children's identifications or activities may compromise their well-being. In this paper, we investigate lightweight solutions that are affordable to edge surveillance systems, which is made feasible and accurate to identify minors such that appropriate privacy-preserving measures can be applied accordingly. State of the art deep learning architectures are modified and re-purposed in a cascaded fashion to maximize the accuracy of our model. A pipeline extracts faces from the input frames and classifies each one to be of an adult or a child. Over 20,000 labeled sample points are used for classification. We explore the timing and resources needed for such a model to be used in the Edge-Fog architecture at the edge of the network, where we can achieve near real-time performance on the CPU. Quantitative experimental results show the superiority of our proposed model with an accuracy of 92.1% in classification compared to some other face recognition based child detection approaches.
