Battery-Free Camera Occupancy Detection System
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2021-06-24
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Association for Computing Machinery (ACM)
Abstract
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.
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This proceeding is published as Saffari, Ali, Sin Yong Tan, Mohamad Katanbaf, Homagni Saha, Joshua R. Smith, and Soumik Sarkar. "Battery-free camera occupancy detection system." In Proceedings of the 5th international workshop on embedded and mobile deep learning, pp. 13-18. 2021. doi: https://doi.org/10.1145/3469116.3470013.