Exploiting scene and body contexts in controlling continuous vision body cameras

Abstract Ever-increasing performance at decreasing price has fueled camera deployments in a wide variety of real-world applications—making the case stronger for battery-powered, continuous-vision camera systems. However, given the state-of-the-art battery technology and embedded systems, most battery-powered mobile devices still do not support continuous vision. In order to reduce energy and storage requirements, there have been proposals to offload energy-demanding computations to the cloud Naderiparizi et al. (2016) [1] , to discard uninteresting video frames Naderiparizi et al. (2017), and to use additional sensors to detect and predict when to turn on the camera Bahl et al. (2012) [2] . However, these proposals either require a fat communication bandwidth or have to sacrifice capturing of important events. In this paper, we present — ZenCam 1 , which is an always-on body camera that exploits readily available information in the encoded video stream from the on-chip firmware to classify the dynamics of the scene. This scene-context is further combined with simple inertial measurement unit (IMU)-based activity level-context of the wearer to optimally control the camera configuration at run-time to keep the device under the desired energy budget. We describe the design and implementation of ZenCam and thoroughly evaluate its performance in real-world scenarios. Our evaluation shows a 29.8%–35% reduction in energy consumption and 48.1-49.5% reduction in storage usage when compared to a standard baseline setting of 1920x1080 at 30fps while maintaining a competitive or better video quality at the minimal computational overhead.