论文标题
具有低分辨率红外传感器和CNN的微控制器的隐私距离监控
Privacy-preserving Social Distance Monitoring on Microcontrollers with Low-Resolution Infrared Sensors and CNNs
论文作者
论文摘要
低分辨率红外（IR）阵列传感器提供低成本，低功率和隐私的替代替代光相机和智能手机/可穿戴设备，用于在室内空间中进行社交距离监控，允许识别基本形状，而无需透露个人的个人详细信息。在这项工作中，我们证明了可以准确检测具有小型卷积神经网络（CNN）的8x8 IR阵列传感器的原始输出。此外，CNN可以直接在基于微控制器（MCU）的传感器节点上执行。 通过新收集的开放数据集的结果，我们表明我们最好的CNN实现了86.3％的平衡精度，这极大地超过了最先进的确定性算法所获得的61％。更改CNN的架构参数，我们获得了一组丰富的帕累托模型，涵盖了70.5-86.3％的精度和0.18-75K参数。这些模型部署在STM32L476RG MCU上，其延迟为0.73-5.33ms，每推理的能量消耗为9.38-68.57μj。
Low-resolution infrared (IR) array sensors offer a low-cost, low-power, and privacy-preserving alternative to optical cameras and smartphones/wearables for social distance monitoring in indoor spaces, permitting the recognition of basic shapes, without revealing the personal details of individuals. In this work, we demonstrate that an accurate detection of social distance violations can be achieved processing the raw output of a 8x8 IR array sensor with a small-sized Convolutional Neural Network (CNN). Furthermore, the CNN can be executed directly on a Microcontroller (MCU)-based sensor node. With results on a newly collected open dataset, we show that our best CNN achieves 86.3% balanced accuracy, significantly outperforming the 61% achieved by a state-of-the-art deterministic algorithm. Changing the architectural parameters of the CNN, we obtain a rich Pareto set of models, spanning 70.5-86.3% accuracy and 0.18-75k parameters. Deployed on a STM32L476RG MCU, these models have a latency of 0.73-5.33ms, with an energy consumption per inference of 9.38-68.57μJ.
