Video big data in smart city: Background construction and optimization for surveillance video processing

被引：45

作者：

Tian, Ling ^{[1
]}

Wang, Hongyu ^{[1
]}

Zhou, Yimin ^{[1
]}

Peng, Chengzong ^{[1
]}

机构：

[1] Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu, Sichuan, Peoples R China

来源：

FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE | 2018年 / 86卷

关键词：

Smart cities; Internet of Things; Video big data; Data fusion; Surveillance video processing; HEVC; STANDARD; FRAME;

D O I：

10.1016/j.future.2017.12.065

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Transforming infrastructures, buildings and services with the sensed data from the Internet of Things (IoT) technique has drawn wide attention. Enormous video data from city surveillance cameras poses huge challenges of transmission, storage and analysis, which necessitates new video compression technologies. The fusion of video data generated from smart city could be used to support city management and urban policy. Based on the specific characteristics of surveillance video, which are successive pictures have very strong correlations and each picture can be divided into background and foreground, this work proposes a block-level background modeling (BBM) algorithm to support long-term reference structure for efficient surveillance video coding. A rate-distortion optimization for surveillance source (SRDO) algorithm is also developed to improve the coding performance. Experimental results show that the proposed BBM and SRDO can significantly improve the compression performance, which can effectively support diverse video applications in smart city. (C) 2018 The Authors. Published by Elsevier B.V.

引用

页码：1371 / 1382

页数：12

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