A Light-Powered Smart Camera With Compressed Domain Gesture Detection

Abstract

This paper presents an ultralow power smart camera with gesture detection. Low power is achieved by directly extracting gesture features from the compressed measurements, which are the block averages and the linear combinations of the image sensor’s pixel values. We present two classifier techniques to allow low computational and storage requirements. The system has been implemented on an analog devices BlackFin ULP vision processor. By enabling ultralow energy consumption, we demonstrate that the system is powered by ambient light harvested through photovoltaic cells whose output is regulated by TI’s dc–dc buck converter with maximum power point tracking. Measured data reveals that with only 400 compressed measurements ( $768\times $ compression ratio) per frame, the system is able to recognize key wake-up gestures with greater than 80% accuracy and only $95mJ$ of energy per frame. Owing to its fully self-powered operation, the proposed system can find wide applications in “always-on” vision systems, such as in surveillance, robotics, and consumer electronics with touch-less operation.