Elsevier

Future Generation Computer Systems

Volume 86, September 2018, Pages 1371-1382
Future Generation Computer Systems

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

https://doi.org/10.1016/j.future.2017.12.065Get rights and content
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open access

Highlights

  • A three-level video data fusion scheme is described for IoT BD fusion.

  • A coding architecture with background picture is proposed for smart city video.

  • A specific coding parameter optimization algorithm obtains notable performance.

Abstract

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.

Keywords

Smart cities
Internet of Things
Video big data
Data fusion
Surveillance video processing

Cited by (0)

Ling Tian received the B.S., M.S., and Ph.D. degrees from the school of computer science and engineering, University of Electronic Science and Technology of China (UESTC) in 2003, 2006 and 2010, respectively. She is currently an Associate Professor in UESTC. She had been a visiting scholar in Georgia State University (GSU) during 2013 in the United States. She has edited 2 books and holds over 10 China patents. She has contributed over 10 technology proposals to the standardizations such as China Audio and Video Standard (AVS) and China Cloud Computing standard. Her research interests include image/video coding, streaming and processing, visual perception and applications.

Hongyu Wang received the B.S. degree from University of Electronic Science and Technology of China (UESTC) in 2016. He is currently working toward B.S. degree in School of Computer Science, UESTC. His research interests include video coding, streaming and processing.

Yimin Zhou got his B.S., M.S., and Ph.D. degrees in computer science from the school computer science and engineering, University of Electronic Science and Technology of China (UESTC) in 2003, 2006, and 2009, respectively. He was a joint Ph.D. student with University of Central Arkansas (UCA), USA, from 2007 to 2009. He was a visiting scholar at Georgia State University (GSU) and University of California, Santa Barbara (UCSB), USA, in 2013 and 2017 separately. He has been a post Ph.D. major in signal processing since 2013. He is currently an Associate Professor in UESTC. His research interests include video coding, streaming and processing. He owns 5 granted parents and concerned the video encoding standards like HEVC, IVC and AVS. His 2 proposals were adopted to the MPEG and over 20 proposals adopted to the AVS.

Chengzong Peng received the B.S. degree from University of California, Irvine (UCI) in 2017. He is currently working as a research assistant in School of Computer Science, UESTC. His research interests include database and big data technologies.