Table of Contents Author Guidelines Submit a Manuscript
Journal of Electrical and Computer Engineering
Volume 2018 (2018), Article ID 3839104, 10 pages
https://doi.org/10.1155/2018/3839104
Research Article

A Retrieval Optimized Surveillance Video Storage System for Campus Application Scenarios

1School of Computer Science, Beijing Information Science and Technology University, Beijing, China
2Beijing Key Laboratory of Internet Culture and Digital Dissemination Research, School of Computer Science, Beijing Information Science and Technology University, Beijing, China

Correspondence should be addressed to Xin Chen; nc.ude.utsib@nixnehc

Received 6 November 2017; Revised 30 January 2018; Accepted 19 February 2018; Published 8 April 2018

Academic Editor: Attila Kertesz

Copyright © 2018 Shengcheng Ma et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. S. D. Cao, Y. Hua, D. Feng, Y. Y. Sun, and P. F. Zuo, “High-Performance distributed storage system for large-scale high-definition video data,” Ruan Jian Xue Bao/Journal of Software, vol. 28, no. 8, 2017, in Chinese. View at Google Scholar
  2. Z. Zhao, X. Cui, and H. Zhang, “Cloud storage technology in video surveillance,” Advanced Materials Research, vol. 532-533, pp. 1334–1338, 2012. View at Publisher · View at Google Scholar · View at Scopus
  3. W. Zheng and X. J. Zhang, “Research and Application of IP-SAN,” Journal of Electrical and Computer Engineering, 2003. View at Google Scholar
  4. J. Calic and E. Izquierdo, “Efficient key-frame extraction and video analysis,” in Proceedings of the International Conference on Information Technology: Coding and Computing, ITCC 2002, pp. 28–33, USA, April 2002. View at Publisher · View at Google Scholar · View at Scopus
  5. J. He, Study on key technique in video surveillance storage system based on IP-SAN, Shanghai, Shanghai Jiaotong University, 2011, in Chinese.
  6. JX. Tang, The software design of storage subsystem for network video surveillance system, Zhejiang University, Zhejiang, 2013, in Chinese.
  7. M. Jiang, Z. Y. Niu, and S. P. Zhang, “Design and implementation of video surveillance storage system,” Computer Engineering and Design, vol. 35, no. 12, pp. 4195–4201, 2014 (Chinese). View at Google Scholar
  8. Z. Z. Sun, Q. X. Zhang, Y. A. Tan, and Y. Z. Li, “Ripple-RAID: A high-performance and energy-efficient RAID for continuous data storage,” Ruan Jian Xue Bao/Journal of Software, vol. 26, no. 7, pp. 1824–1839, 2015 (Chinese). View at Google Scholar
  9. J. Y. Wu, Y. Gu, D. P. Ju, and D. S. Wang, “THNVR: Distributed large-scale surveillance video storage system,” Computer Engineering and Applications, vol. 45, no. 31, pp. 56–59, 2009 (Chinese). View at Google Scholar
  10. Q. M. Le, A. Amer, and J. Holliday, “SMR Disks for Mass Storage Systems,” in Proceedings of the IEEE 23rd International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems, MASCOTS 2015, pp. 228–231, USA, October 2015. View at Publisher · View at Google Scholar · View at Scopus
  11. P. Mishra, M. Mishra, and A. K. Somani, “Bulk I/O storage management for big data applications,” in Proceedings of the 24th IEEE International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems, MASCOTS 2016, pp. 412–417, UK, September 2016. View at Publisher · View at Google Scholar · View at Scopus
  12. C.-F. Lin, M.-C. Leu, S.-M. Yuan, and C.-T. Tsai, “A framework for scalable cloud video recorder system in surveillance environment,” in Proceedings of the 2012 9th International Conference on Ubiquitous Intelligence & Computing and 9th International Conference on Autonomic & Trusted Computing (UIC/ATC), pp. 655–660, IEEE, Fukuoka, September 2012. View at Publisher · View at Google Scholar · View at Scopus
  13. H. E. Xiao-Feng, Analysis and Application of Network Video Recorder (NVR) Storage Technology, China Science & Technology Information, 2011.
  14. Y. Gu, X. Wang, S. Shen et al., “Analysis of data storage mechanism in NoSQL database MongoDB,” in Proceedings of the 2nd IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2015, pp. 70-71, June 2015. View at Publisher · View at Google Scholar · View at Scopus
  15. P. Zhou, F. Dong, Z. Xu, J. Zhang, R. Xiong, and J. Luo, “ECStor: A Flexible Enterprise-Oriented Cloud Storage System Based on GlusterFS,” in Proceedings of the 4th International Conference on Advanced Cloud and Big Data, CBD 2016, pp. 13–18, China, August 2016. View at Publisher · View at Google Scholar · View at Scopus
  16. G. Liu and J. Zhao, “Key frame extraction from MPEG video stream,” in Proceedings of the 3rd International Symposium on Information Processing, ISIP 2010, pp. 423–427, China, November 2010. View at Publisher · View at Google Scholar · View at Scopus
  17. Y. Yang, F. Dadgostar, C. Sanderson, and B. C. Lovell, “Summarisation of surveillance videos by key-frame selection,” in Proceedings of the 2011 5th ACM/IEEE International Conference on Distributed Smart Cameras, ICDSC 2011, Belgium, August 2011. View at Publisher · View at Google Scholar · View at Scopus
  18. R. Bradski G and A. Kaehler, Learning OpenCV, OReilly Media, 2014.
  19. A. Horváth, M. Paolieri, L. Ridi, and E. Vicario, “Transient analysis of non-Markovian models using stochastic state classes,” Performance Evaluation, vol. 69, no. 7-8, pp. 315–335, 2012. View at Publisher · View at Google Scholar · View at Scopus
  20. S. Balsamo, P. G. Harrison, and A. Marin, “Methodological construction of product-form stochastic Petri nets for performance evaluation,” The Journal of Systems and Software, vol. 85, no. 7, pp. 1520–1539, 2012. View at Publisher · View at Google Scholar · View at Scopus
  21. M. K. Molloy, “Performance Analysis Using Stochastic Petri Nets,” IEEE Transactions on Computers, vol. C-31, no. 9, pp. 913–917, 1982. View at Publisher · View at Google Scholar · View at Scopus
  22. A. Marin, S. Balsamo, and P. G. Harrison, “Analysis of stochastic Petri nets with signals,” Performance Evaluation, vol. 69, no. 11, pp. 551–572, 2012. View at Publisher · View at Google Scholar · View at Scopus
  23. S. Distefano, F. Longo, and M. Scarpa, “Marking dependency in non-Markovian stochastic Petri nets,” Performance Evaluation, vol. 110, pp. 22–47, 2017. View at Publisher · View at Google Scholar · View at Scopus
  24. W. M. Zuberek, “Performance evaluation using unbounded timed Petri nets,” in Proceedings of the Third International Workshop on Petri Nets and Performance Models (PNPM89), pp. 180–186, 1989. View at Scopus
  25. P. Bonet, C. Llado M, R. Puigjaner et al., PIPE v2.5: a Petri Net Tool for Performance Modeling, 2007.
  26. C. Lin, Stochastic Petri Nets and System Performance Evaluation, Tsinghua University Press, Beijing, China, 2nd edition, 2005.