Surveillance Video Coding for Traffic Scene based on Vehicle Knowledge and Shared Library by Cloud-Edge Computing in Cyber-Physical-Social Systems

Abstract

With rapid development of intelligent video surveillance systems based on cloud computing devices and edge computing devices in Cyber-Physical-Social Systems, massive surveillance video data has brings enormous challenge for video storage and transmission. However, existing surveillance video coding approaches hardly utilize intelligent video analysis results for improving video coding. This paper proposed a surveillance video coding scheme for traffic scene based on vehicle knowledge and shared library by cloud-edge computing in Cyber-Physical-Social Systems. Firstly, in order to provide the object library for synchronous application at the encode and decode side offline, a generation method of shared long-term foreground reference object library is proposed by using the existing large-scale monitoring vehicle object datasets. Then, to meet the requirement of low complexity and high-performance coding, a virtual foreground reference picture generation method with coding-oriented object retrieval is proposed. Experimental results show that the proposed scheme can obtain the satisfactory effect of the virtual foreground reference picture. Also, it can yield remarkable bit rate reductions, compared to HEVC.