%ASaffari, Ali [University of Washington, Iowa State University]%ATan, Sin [University of Washington, Iowa State University]%AKatanbaf, Mohamad [University of Washington]%ASaha, Homagni [Iowa State University]%ASmith, Joshua [University of Washington]%ASarkar, Soumik [Iowa State University]%D2021%I %K %MOSTI ID: 10303866 %PMedium: X %TBattery-Free Camera Occupancy Detection System %XOccupancy 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. Country unknown/Code not availablehttps://doi.org/10.1145/3469116.3470013OSTI-MSA