ZenCam: Context-Driven Control of Autonomous Body Cameras

In this paper, we present — ZenCam, 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.