Cluster Computing

, Volume 19, Issue 3, pp 1283–1292 | Cite as

The big data analytics and applications of the surveillance system using video structured description technology

  • Zheng Xu
  • Lin Mei
  • Chuanping Hu
  • Yunhuai Liu


Recently, the video data has very huge volume, taking one city for example, thousands of cameras are built of which each collects high-definition video over 24–48 GB every day with the rapidly growth; secondly, data collected includes variety of formats involving multimedia, images and other unstructured data; furthermore the valuable information contains in only a few frames called key frames of massive video data; and the last problem caused is how to improve the processing velocity of a large amount of original video with computers, so as to enhance the crime prediction and detection effectiveness of police and users. In this paper, we conclude a novel architecture for next generation public security system, and the “front + back” pattern is adopted to address the problems brought by the redundant construction of current public security information systems which realizes the resource consolidation of multiple IT resources, and provides unified computing and storage environment for more complex data analysis and applications such as data mining and semantic reasoning. Under the architecture, we introduce cloud computing technologies such as distributed storage and computing, data retrieval of huge and heterogeneous data, provide multiple optimized strategies to enhance the utilization of resources and efficiency of tasks. This paper also presents a novel strategy to generate a super-resolution image via multi-stage dictionaries which are trained by a cascade training process. Extensive experiments on image super-resolution validate that the proposed solution can get much better results than some state-of-the-arts ones.


Big data analysis Public security Video surveillance system 



This work was supported in part by the National Science and Technology Major Project under Grant 2013ZX01033002-003, in part by the National High Technology Research and Development Program of China (863 Program) under Grant 2013AA014603, in part by the National Natural Science Foundation of China under Grant 61300202, and in part by the Natural Science Foundation of Shanghai under Grant 13ZR1452900.


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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.The Third Research Institute of the Ministry of Public SecurityShanghaiChina

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