Battery-Free Camera Occupancy Detection System
Battery-Free Camera Occupancy Detection System
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DOI：
10.1145/3469116.3470013
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发表时间：
2021-06
期刊：
影响因子：
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通讯作者：
Ali Saffari;Sin Yong Tan;Mohamad Katanbaf;Homagni Saha;Joshua R. Smith;S. Sarkar
中科院分区：
文献类型：
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作者：
Ali Saffari;Sin Yong Tan;Mohamad Katanbaf;Homagni Saha;Joshua R. Smith;S. Sarkar
占用检测系统通常配备有高质量的相机和具有高计算能力的处理器以运行检测算法。本文介绍了一种人体占用检测系统，该系统使用无电池摄像头和在低成本集线器上实现的深度学习模型来检测人体存在。我们的低分辨率相机从环境光中获取能量，并使用反向散射通信将数据传输到集线器。我们在Raspberry Pi 4 Model B上实现了最先进的YOLOv5网络检测算法，该算法具有高检测精度和快速推理速度。我们在Raspberry Pi上仅用2GB CPU RAM就实现了每个图像约100 ms的推理速度和>90%的整体检测准确率。在实验结果中，我们还证明了检测是鲁棒的噪声，照度，遮挡，和角度的抑郁症。
Occupancy detection systems are commonly equipped with high-quality cameras and a processor with high computational power to run detection algorithms. This paper presents a human occupancy detection system that uses battery-free cameras and a deep learning model implemented on a low-cost hub to detect human presence. Our low-resolution camera harvests energy from ambient light and transmits data to the hub using backscatter communication. We implement the state-of-the-art YOLOv5 network detection algorithm that offers high detection accuracy and fast inferencing speed on a Raspberry Pi 4 Model B. We achieve an inferencing speed of ~ 100ms per image and an overall detection accuracy of >90% with only 2GB CPU RAM on the Raspberry Pi. In the experimental results, we also demonstrate that the detection is robust to noise, illuminance, occlusion, and angle of depression.