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Database as a Service for Cloud Based Video Surveillance System

  • Sumit KumarEmail author
  • Vasudeva Rao Prasadula
  • Shivakumar Murugesh
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 98)

Abstract

In recent times, cloud computing has become the widely accepted technology for moving the database over the cloud, which has brought revolution in the IT industry, this term is recently coined as Database as a Service (DBaaS). Mostly cloud databases are used for data intensive applications such as data warehousing, data mining and monitoring purpose. On the advent of smart cities concept, massively scalable video surveillance has become the necessity in public area. In this paper, we proposed a database as a service to support video surveillance system on the cloud environment with real time video analytics. In this regard, we have explored Trove (Open stack cloud DBaaS component) to facilitate scalability and availability for the video surveillance system furthermore we have also proposed data processing architecture based on open CV, Apache Kafka and Apache spark.

Keywords

Cloud computing Database as a Service (DBaaS) Trove Apache Spark Apache Kafka 

References

  1. 1.
    Abdullah, T., Anjum, A., Tariq, M., Baltaci, Y., Antonopoulos, N.: Traffic monitoring using video analytics in clouds. In: 7th IEEE/ACM International Conference on Utility and Cloud Computing (UCC), pp. 39–48 (2014)Google Scholar
  2. 2.
    MacDorman, K.F., Nobuta, H., Koizumi, S., Ishiguro, H.: Memory based attention control for activity recognition at a subway station. IEEE Multimed. 14(2), 38–49 (2007)CrossRefGoogle Scholar
  3. 3.
    Stauffer, C., Grimson, W.E.L.: Learning patterns of activity using real-time tracking. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 747–757 (2000)CrossRefGoogle Scholar
  4. 4.
    Stauffer, C., Grimson, W.: Adaptive background mixture models for real-time tracking. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 246–252 (1999)Google Scholar
  5. 5.
    Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 511–518 (2001)Google Scholar
  6. 6.
    Lin, Y., Lv, F., Zhu, S., Yang, M., Cour, T., Yu, K., Cao, L., Huang, T.: Large-scale image classification: fast feature extraction and SVM training. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2011)Google Scholar
  7. 7.
  8. 8.
  9. 9.
    Shende, S.B., Chapke, P.P.: Cloud database management system (CDBMS). Compusoft 4(1), 1462 (2015)Google Scholar
  10. 10.
    Zheng, X., Fu, M., Chugh, M.: Big data storage and management in SaaS applications. J. Commun. Inform. Netw. 2(3), 18–29 (2017)CrossRefGoogle Scholar
  11. 11.
    Bosh Security System: IVA 5.60 intelligent video analysis. Bosh Security System, Technical report (2014)Google Scholar
  12. 12.
    Feng, J., Wen, P., Liu, J., Li, H.: Elastic stream cloud (ESC): a stream-oriented cloud computing platform for rich internet application. In: International Conference on High Performance Computing and Simulation (2010)Google Scholar
  13. 13.
    Network deployment chart from NUUO CMS illustration. http://gsf.com.my/dvr/NUUO/NUUOcms.htm. Accessed June 2012
  14. 14.
    VGuard. http://www.vguardinternational.com/cms/. Accessed 26 Feb 2012
  15. 15.
    Amazon S3 Storage Service. http://aws.amazon.com/es/s3/. Accessed 26 Feb 2012
  16. 16.
    Nikam, V., Meshram, B.B.: Parallel and scalable rules based classifier using map-reduce paradigm on hadoop cloud. Int. J. Adv. Technol. Eng. Sci. 02(08), 558–568 (2014)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Sumit Kumar
    • 1
    Email author
  • Vasudeva Rao Prasadula
    • 1
  • Shivakumar Murugesh
    • 1
  1. 1.Bharat Electronics LimitedBangaloreIndia

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