Adaptive Methodologies for Energy-Efficient Object Detection and Tracking With Battery-Powered Embedded Smart Cameras

Abstract

Battery-powered wireless embedded smart cameras have limited processing power, memory and energy. Since video processing tasks consume considerable amount of energy, it is essential to have lightweight algorithms to increase the energy efficiency of camera nodes. Moreover, just grabbing and buffering a frame require significant amount of energy. Thus, it is not sufficient to only focus on the vision algorithms. Methodologies are needed to determine when and how long a camera can be idle. In this paper, we first present a feedback method for detection and tracking, which provides significant savings in processing time. We take advantage of these savings by sending the microprocessor to idle state at the end of processing a frame. Then, we present an adaptive methodology that can send the camera to idle state not only when the scene is empty but also when there are target objects. Idle state duration is adaptively changed based on the speeds of tracked objects. We then introduce a combined method that employs the feedback method and the adaptive methodology together, and provides further savings in energy consumption. We provide a detailed comparison of these methods, and present experimental results showing the gains in processing time as well as the significant savings in energy consumption and increase in battery life.