论文标题
低分辨率红外传感器和适应性推理的节能和隐私感知的社会距离监测
Energy-efficient and Privacy-aware Social Distance Monitoring with Low-resolution Infrared Sensors and Adaptive Inference
论文作者
论文摘要
低分辨率红外（IR）传感器与机器学习（ML）相结合，可以利用在室内空间中实施隐私的社会距离监控解决方案。但是，需要在物联网（IoT）边缘节点上执行这些应用程序，因此能耗至关重要。在这项工作中，我们提出了一种节能自适应推理解决方案，该解决方案由简单的唤醒触发器和8位量化的卷积神经网络（CNN）组成，该解决方案仅用于难以分类的帧。在物联网微控制器上部署这种自适应系统，我们表明，在处理8x8低分辨率IR传感器的输出时，我们能够将基于静态CNN的方法降低37-57％，而精确度下降了低于2％（83％均衡精度）。
Low-resolution infrared (IR) Sensors combined with machine learning (ML) can be leveraged to implement privacy-preserving social distance monitoring solutions in indoor spaces. However, the need of executing these applications on Internet of Things (IoT) edge nodes makes energy consumption critical. In this work, we propose an energy-efficient adaptive inference solution consisting of the cascade of a simple wake-up trigger and a 8-bit quantized Convolutional Neural Network (CNN), which is only invoked for difficult-to-classify frames. Deploying such adaptive system on a IoT Microcontroller, we show that, when processing the output of a 8x8 low-resolution IR sensor, we are able to reduce the energy consumption by 37-57% with respect to a static CNN-based approach, with an accuracy drop of less than 2% (83% balanced accuracy).
