Energy-efficient computer vision is vitally important for embedded and mobile platforms where a longer battery life can allow increased deployment in the field. In image sensors, one of the primary causes of energy expenditure is the sampling and digitization process. Smart subsampling of the image-array in a manner that is task-specific, can result in significant savings of energy. We present an adaptive algorithm for video subsampling, which is aimed at enabling accurate object detection, while saving sampling energy. The approach utilizes objectness measures, which we show can be accurately estimated even from sub-sampled frames, and then uses that information to determine the adaptive sampling for the subsequent frame. We show energy savings of 18 - 67% with only a slight degradation in object detection accuracy in experiments. These results motivated us to further explore energy-efficient subsampling using advanced techniques such as, reinforcement learning and Kalman filtering. The experiments using these techniques are underway and provide ample support for adaptive subsampling as a promising avenue for embedded computer vision in the future.

In this work in progress paper, we describe an REU summer experience on imaging sensors that involved a female junior level Electrical Engineering student, a graduate student advisor, and three faculty. A research plan was designed to embed the student in a sensor and machine learning research with specific emphasis on energy-efficient cameras. The motivation for submitting this paper is the unique planning and the quality of the overall student experience which resulted in continuous engagement of the REU student with the faculty after the REU summer program completed. The program resulted in a major presentation at an international event, an NSF I/UCRC poster presentation, a research conference submission which is remarkable for an undergraduate student, and finally a new research direction for the graduate mentor and faculty. This paper describes successful strategies for research engagement for undergraduates in state-of-the-art research fields which yield positive outcomes for all participants, and is grounded in contemporary educational methodology and theoretical frameworks.