A Light-powered, Always-On, Smart Camera with Compressed Domain Gesture Detection
This enables fully self-powered, always-on vision systems for applications like surveillance and consumer electronics, though it is incremental as it builds on existing compressed sensing and gesture recognition methods.
The paper tackles the problem of energy consumption in always-on camera systems by proposing a light-powered gesture recognition system that extracts features directly from compressed measurements, achieving over 80% accuracy with only 95mJ per frame at a 768x compression ratio.
In this paper we propose an energy-efficient camera-based gesture recognition system powered by light energy for "always on" applications. Low energy consumption 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. The gestures are recognized using a nearest-neighbour (NN) classifier followed by Dynamic Time Warping (DTW). The system has been implemented on an Analog Devices Black Fin ULP vision processor and powered by PV cells whose output is regulated by TI's DC-DC buck converter with Maximum Power Point Tracking (MPPT). Measured data reveals that with only 400 compressed measurements (768x 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.