Design and Implementation of a Prototype Cloud Video Surveillance System
Yong-Hua Xiong, Shao-Yun Wan, Yong He,
and Dan Su
School of Information Science and Engineering, Central South University, Yuelu District, Changsha, 410083, China
Cloud-based video surveillance systems, as a new cloud computing service model, are an emerging research topic, both at home and abroad. Current research is mainly focused on exploring applications of the system. This paper proposes a design and implementation method for cloud-based video surveillance systems using the characteristics of cloud computing, such as parallel computing, large storage space, and easy expandability. The system architecture and function modules are built, and a prototype cloud-based video surveillance system is established in a campus network using key technologies, including virtual machine task access control, video-data distributed storage, and database-active communicationmethods. Using the system, the user is able to place a webcam in a location that requires monitoring so that video surveillance can be achieved, and video data can be viewed through a browser. The system has the following advantages: low investment and maintenance cost, high portability, easily extendable, superior data security, and excellent sharing. As a private cloud server in the campus network, the system is able to not only provide convenient video surveillance services, but it can also be an excellent practical experimental platform for cloud computing-related research, which carries outstanding application value.
-  P. Mell, T. Grance, R. M. Blank, and P. D. Gallagher, “The NIST Definition of Cloud Computing,“ National Institute of Standards and Technology, Special Publication 800-145, 2009.
-  X. Yang, T. Pan, and J. Shen, “On 3G Mobile E-commerce Platform Based on Cloud Computing,“ Proc. of IEEE Int. Conf. on Ubi-Media Computing, pp. 198-201, 2010.
-  H. E. Schaffer, S. F. Averitt, M. I. Hoit, A. Peeler, E. D. Sills, and M. A. Vouk, “NCSU‘s Virtual Computing Lab: A Cloud Computing Solution,“ IEEE Computer Society, pp. 94-97, 2009.
-  L. Li, X. Li, S. Youxia, and L.Wen, “Research On Mobile Multimedia Broadcasting Service Integration Based On Cloud Computing,“ Int. Conf. on Multimedia Technology (ICMT), 2010.
-  P. Simoens, F. De Turck, B. Dhoedt, and P. Demeester, “Remote display solutions for mobile cloud computing,“ Computer, Vol.44, No.8, pp. 46-53, 2011.
-  http://www.cloudsurveillance.com, 2012.
-  http://www.aspice.eu, 2012
-  http://www.ccidcom.com/html/chanpinjishu/yewu/shipinjiankong/201111/10-161041.html, 2012.
-  Q. Li, T. Zhang, and Y. Yu, “Using cloud computing to process intensive floating car data for urban traffic surveillance,“ Int. J. of Geographical Information Science, Vol.25, No.8, pp. 1303-1322, 2011.
-  Y. Wen, X. K. Yang, and Y. Xu, “Cloud-computing-based framework for multi-camera topology inference in smart city sensing system,“ Proc. of 2010 ACM multimedia workshop on Mobile cloud media computing, pp. 65-70, 2010.
-  http://blog.csdn.net/mingojiang/article/details/7908756, 2012.
-  http://technet.microsoft.com/zh-en/library/bb522893,2008.
-  D. Vance, “Supporting Active Database Semantics in Sybase,“ University of Florida, 1996.
-  J. Leverich and C. Kozyrakis, “On the energy (in) efficiency of Hadoop clusters,“ ACM SIGOPS Operating Systems Review, Vol.44, No.1, pp. 61-65, 2010.
-  Y. Chen, A. S. Ganapathi, A. Fox, R. H. Katz, and D. A. Patterson, “Statistical workloads for energy efficient mapreduce,“ UCB/EECS-2010-6. Berkeley: University of California, 2010.
-  B. M. Oppenheim, “Reducing cluster power consumption by dynamically suspending idle nodes,“ [MS. Thesis], 2010.
-  Y. Chen, L. Keys, and R. H. Katz, “Towards energy efficient MapReduce,“ UCB/EECS-2009-109. Berkeley: University of California, 2009.