Resource-aware video processing techniques for ambient multimedia systems

Ambient Intelligence (AmI) is the inconspicuous presence of computing into every facet of our lives. AmI systems of the future will contain devices with highly limited resources in terms of processing power, memory, and battery lifetime. Contrary to this are the memory, power, and cycle-hungry video processing applications which are required to provide the level of utility demanded by the users. This work introduces the idea of processing a portion of a video frame with the intent of achieving high levels of video processing performance while reducing the hardware requirements for that processing considerably. The techniques we propose operate only on a portion of the video frame and optionally adjust that portion's size dynamically to reasonably match the video content. Our results show that this technique can save roughly 75% in both memory and processing cycle requirements and up to 87% in energy consumption while inducing less than 10% error in XY processing of the video frame.