Cloud-assisted analysis for energy efficiency in intelligent video systems

The prevalence of intelligent video systems such as urban video surveillance or Google Glass, is gradually changing our daily life. This type of systems applies online analysis on video streams for the extraction of object information, which will be utilized to provide abundant content-based services. However, the system also brings challenges to the system resource utilization, while providing convenience to users. The online video analysis requires continuous and immediate processing of video streams, which always causes massive investment on the processing hardware and intolerable power consumption. In this paper, we propose to utilize the power of cloud to improve the energy efficiency of intelligent video systems, through video stream consolidation based on the fluctuation characteristic of analysis workloads. Our trace-driven study proves that the pressure on the power consumption can be significantly alleviated, while ensuring the processing ability in practical scenes.