Our paper has been accepted for publication at IPSN ‘19 (Information Processing in Sensor Networks): BackCam, Wireless Computer Vision Using Commodity Devices by Colleen Josephson, Lei Yang, Pengyu Zhang and Sachin Katti.
The code and hardware design are fully open-sourced on github!
We introduce the design and implementation of BackCam, a low-power wireless camera sensor platform that supports continuous realtime vision applications, all using commodity radios. In the lowest power mode, our camera board consumes only 9.7mW and continuously transmits images for over one month on two AA batteries. We introduce a novel power management system that incorporates input from the camera itself to increase battery life up to 62%. Using images and system metadata as input, we designed a feedback system between the sensor and the gateway. This allows dynamic vision application requirements to be met while consuming as little power as possible. For example, our system can temporarily increase the resolution after an object of interest is detected, then reduce it again after it has disappeared. This increases the accuracy of simplistic facial recognition by at least 25% compared to operating constantly in the lowest power mode. We implement communications using a full-duplex WiFi backscatter radio, ensuring compatibility with commodity WiFi devices. We also designed an efficient data streaming and compression pipeline straight from the camera to the backscatter transmitter, allowing us to minimize latency and avoid expensive memory writes. We deployed BackCam in a real office environment, and as a proof-of-concept, implemented basic realtime face detection and recognition.