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.

中文翻译：

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网络-物理-社会系统中基于车辆知识和共享库的交通场景监控视频编码

随着网络-物理-社会系统中基于云计算设备和边缘计算设备的智能视频监控系统的快速发展，海量监控视频数据给视频存储和传输带来了巨大挑战。然而，现有的监控视频编码方法很难利用智能视频分析结果来改进视频编码。本文提出了一种基于车辆知识和基于云边缘计算共享库的网络-物理-社会系统中的交通场景监控视频编码方案。首先，为了离线提供编解码端同步应用的对象库，利用现有的大规模监控车辆对象数据集，提出一种共享长期前景参考对象库的生成方法。然后，为满足低复杂度和高性能编码的要求，提出一种面向编码对象检索的虚拟前景参考图生成方法。实验结果表明，所提方案能够获得满意的虚拟前景参考图效果。此外，与 HEVC 相比，它可以显着降低比特率。