Skip to main content
SpringerLink
Log in

Automated wireless video surveillance: an evaluation framework

  • Special Issue Paper
  • Published:
Journal of Real-Time Image Processing Aims and scope Submit manuscript

Abstract

In the past years, surveillance systems have attracted both industries and researchers due to its importance for security. Automated Video Surveillance (AVS) systems are established to automatically monitor objects in real-time. Employing wireless communication in an AVS system is an attractive solution due to its convenient installation and configuration. Unfortunately, wireless communication, in general, has limited bandwidth, not to mention the intrinsic dynamic conditions of the network (e.g., collision and congestion). Many solutions have been proposed in the literature to solve the bandwidth allocation problem in wireless networks, but much less work is done to design evaluation frameworks for such solutions. This paper targets the demand for a realistic wireless AVS system simulation framework that models and simulates most of the details in a typical wireless AVS framework. The proposed simulation framework is built over the well-known NS-3 network simulator. This framework also supports the testing and the evaluation of cross-layer solutions that manages many factors over different layers of AVS systems in the wireless 802.11 infrastructure network. Moreover, the simulation framework supports the collection of many used performance metrics that are usually used in AVS system performance evaluation.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

Notes

  1. https://www.nsnam.org/docs/models/html/wifi-design.html#scope-and-limitations.

References

  1. Norris, C.: A review of the increased use of cctv and video-surveillance for crime prevention purposes in europe. EU Policy Department C Citizens’ Rights and Constitutional Affairs, PE 419, (2009)

  2. Fleck, S., Strasser, W.: Smart camera based monitoring system and its application to assisted living. Proc. IEEE 96(10), 1698–1714 (2008). doi:10.1109/JPROC.2008.928765

    Article  Google Scholar 

  3. Ben Hamida, A., Koubaa, M., Nicolas, H., Amar, C.B.: Video surveillance system based on a scalable application-oriented architecture. Multimed. Tools Appl. pp. 1–27 (2015). doi:10.1007/s11042-015-2987-5

  4. Xu, Z., Hu, C., Mei, L.: Video structured description technology based intelligence analysis of surveillance videos for public security applications. Multimed. Tools Appl. (2015). doi:10.1007/s11042-015-3112-5

    Google Scholar 

  5. Zhizhuo, S., Yu-An, T., Yuanzhang, L.: An energy-efficient storage for video surveillance. Multimed. Tools Appl. 73(1), 151–167 (2014). doi:10.1007/s11042-012-1262-2

    Article  Google Scholar 

  6. Kim, D., Hwang, E., Rho, S.: Multi-camera-based security log management scheme for smart surveillance. Secur. Commun. Netw. 7(10), 1517–1527 (2014)

    Google Scholar 

  7. Mehmood, I., Sajjad, M., Rho, S., Baik, S.W.: Divide-and-conquer based summarization framework for extracting affective video content. Neurocomputing 174(Part A), 393– 403 (2016). doi:10.1016/j.neucom.2015.05.126. http://www.sciencedirect.com/science/article/pii/S0925231215012527

  8. Park, Jh, Rho, S., Jeong, Cs: Real-time robust 3d object tracking and estimation for surveillance system. Secur. Commun. Netw. 7(10), 1599–1611 (2014)

    Google Scholar 

  9. Subudhi, B.N., Ghosh, S., Nanda, P.K., Ghosh, A.: Moving object detection using spatio-temporal multilayer compound markov random field and histogram thresholding based change detection. Multimed. Tools Appl. (2016). doi:10.1007/s11042-016-3698-2

    Google Scholar 

  10. Xu, X., Mu, N., Chen, L., Zhang, X.: Hierarchical salient object detection model using contrast-based saliency and color spatial distribution. Multimed. Tools Appl. 75(5), 2667–2679 (2016). doi:10.1007/s11042-015-2570-0

    Article  Google Scholar 

  11. Zhang, Y., Jiang, F., Rho, S., Liu, S., Zhao, D., Ji, R.: 3d object retrieval with multi-feature collaboration and bipartite graph matching. Neurocomputing 195, 40–49 (2016). doi:10.1016/j.neucom.2015.09.118, http://www.sciencedirect.com/science/article/pii/S0925231216000990. (Learning for Medical Imaging)

  12. Alsmirat, M., Sarhan, N.: Cross-layer optimization and effective airtime estimation for wireless video streaming. In: 2012 21st International Conference on Computer Communications and Networks (ICCCN), pp. 1–7 (2012). doi:10.1109/ICCCN.2012.6289275

  13. Opnet modeler web page. http://www.opnet.com/solutions/networkrd/modeler.html

  14. The network simulator ns-3. http://www.nsnam.org/

  15. IEEE standard for information technology—telecommunications and information exchange between systems—local and metropolitan area networks—specific requirements—part 11: wireless lan medium access control (mac) and physical layer (phy) specifications. ANSI/IEEE Std 802.11, 1999 Edition (R2003) pp. i–513 (2003). doi:10.1109/IEEESTD.2003.95617

  16. Ieee 802.11 amendment to support quality of service. IEEE 802.11e-2005 (2005)

  17. The network simulator ns-2. http://www.isi.edu/nsnam/ns/

  18. Recommendation 500-10: Methodology for the subjective assessment of the quality of television pictures. ITU-R Recommendation BT.500-10 (2000)

  19. Ke, C.H., Shieh, C.K., Hwang, W.S., Ziviani, A., et al.: An evaluation framework for more realistic simulations of mpeg video transmission. J. Inf. Sci. Eng. 24(2), 425–440 (2008)

    Google Scholar 

  20. Misra, S., Reisslein, M., Xue, G.: A survey of multimedia streaming in wireless sensor networks. IEEE Commun. Surv. Tutor. 10(4), 18–39 (2008)

    Article  Google Scholar 

  21. van Der Schaar, M., Sai, S.N.: Cross-layer wireless multimedia transmission: challenges, principles, and new paradigms. IEEE Wirel. Commun. 12(4), 50–58 (2005)

    Article  Google Scholar 

  22. Ortega, A., Ramchandran, K.: Rate-distortion methods for image and video compression. IEEE Signal Process. Mag. 15(6), 23–50 (1998)

    Article  Google Scholar 

  23. Symington, A., Kritzinger, P.: A hardware test bed for measuring ieee 802.11 g distribution coordination function performance. In: IEEE International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems, 2009. MASCOTS’09., pp. 1–7. IEEE (2009)

  24. Cmu/mit image set. http://vasc.ri.cmu.edu/idb/html/face/frontal_images/. Accessed Nov 2015

  25. Georgia tech face database. http://www.anefian.com/research/face_reco.htm. Accessed Nov 2015

  26. Shirani, S., Kossentini, F., Ward, R.: Reconstruction of baseline jpeg coded images in error prone environments. IEEE Trans. Image Process. 9(7), 1292–1299 (2000). doi:10.1109/83.847842

    Article  Google Scholar 

Download references

Acknowledgments

This work is funded in part by the Jordan University of Science and Technology Deanship of Research Grant Number 20140245.

Author information

Authors and Affiliations

  1. Department of Computer Science, Jordan University of Science and Technology, Irbid, Jordan

    Mohammad A. Alsmirat, Yaser Jararweh & Islam Obaidat

  2. National Institute of Technology Kurukshetra, Kurukshetra, India

    Brij B. Gupta

Authors
  1. Mohammad A. Alsmirat
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yaser Jararweh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Islam Obaidat
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Brij B. Gupta
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohammad A. Alsmirat.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Alsmirat, M.A., Jararweh, Y., Obaidat, I. et al. Automated wireless video surveillance: an evaluation framework. J Real-Time Image Proc 13, 527–546 (2017). https://doi.org/10.1007/s11554-016-0631-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11554-016-0631-x

Keywords

Search

Navigation