Weijun Wang, Institute for AI Industry Research (AIR), Tsinghua University; Liang Mi, Shaowei Cen, and Haipeng Dai, State Key Laboratory for Novel Software Technology, Nanjing University; Yuanchun Li, Institute for AI Industry Research (AIR), Tsinghua University; Xiaoming Fu, University of Göttingen; Yunxin Liu, Institute for AI Industry Research (AIR), Tsinghua University
Video analytics is widespread in various applications serving our society. Recent advances of content enhancement in video analytics offer significant benefits for the bandwidth saving and accuracy improvement. However, existing content-enhanced video analytics systems are excessively computationally expensive and provide extremely low throughput. In this paper, we present region-based content enhancement, that enhances only the important regions in videos, to improve analytical accuracy. Our system, RegenHance, enables high-accuracy and high-throughput video analytics at the edge by 1) a macroblock-based region importance predictor that identifies the important regions fast and precisely, 2) a regionaware enhancer that stitches sparsely distributed regions into dense tensors and enhances them efficiently, and 3) a profile-based execution planer that allocates appropriate resources for enhancement and analytics components. We prototype RegenHance on five heterogeneous edge devices. Experiments on two analytical tasks reveal that region-based enhancement improves the overall accuracy of 10-19% and achieves 2-3× throughput compared to the state-of-the-art frame-based enhancement methods.
