Skip to main content
SpringerLink
Log in

Multi-Threshold-Based Frame Segmentation for Content-Aware Video Coding in WMSN

  • Conference paper
  • First Online:
Advances in Computing Systems and Applications (CSA 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 513))

Included in the following conference series:

Abstract

Wireless Visual Sensor Networks have gained relevant interest in the last years due to the many advantages they show. These advantages are characterized by the efficiency of WVSN to offer accurate monitoring of critical zones with a low budget. Nevertheless, WVSN still suffer from a high amount of data when dealing with images and videos that make them not feasible in terms of lifetime. Hence, the optimization of the transmission energy and bitrate has been always a challenging task for many recent works. We advance the state-of-the-art by proposing a low bitrate technique for video coding in WVSN. The proposed method aims to apply a Region-of-Interest Based video coding in an Analyze-Then-Compress Paradigm. We detect the regions of interest where high movement occurs. Then, the compression parameters are set based on region importance. We analyze the efficiency of the proposed method using several evaluation metrics. The proposed technique shows promoting results for very low bitrate WMSN systems.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Akyildiz, I.F., Su, W., Sankarasubramaniam, Y., Cayirci, E.: Wireless sensor networks: a survey. Comput. Netw. 38(4), 393–422 (2002)

    Article  Google Scholar 

  2. Alsmirat, M.A., Jararweh, Y., Obaidat, I., Gupta, B.B.: Automated wireless video surveillance: an evaluation framework. J. Real-Time Image Proc. 13(3), 527–546 (2017)

    Article  Google Scholar 

  3. Boulmaiz, A., Doghmane, N., Harize, S., Kouadria, N., Messadeg, D.: The use of WSN (wireless sensor network) in the surveillance of endangered bird species. In: Advances in Ubiquitous Computing, pp. 261–306. Elsevier (2020)

    Google Scholar 

  4. Chaple, G.N., Daruwala, R.D., Gofane, M.S.: Comparisions of Robert, Prewitt, Sobel operator based edge detection methods for real time uses on FPGA. In: 2015 International Conference on Technologies for Sustainable Development (ICTSD), pp. 1–4 (2015). https://doi.org/10.1109/ICTSD.2015.7095920

  5. Chien, S.Y., Ma, S.Y., Chen, L.G.: Efficient moving object segmentation algorithm using background registration technique. IEEE Trans. Circuits Syst. Video Technol. 12(7), 577–586 (2002)

    Article  Google Scholar 

  6. Harize, S., Mefoued, A., Kouadria, N., Doghmane, N.: HEVC transforms with reduced elements bit depth. Electron. Lett. 54(22), 1278–1280 (2018)

    Article  Google Scholar 

  7. Karray, F., Jmal, M.W., Garcia-Ortiz, A., Abid, M., Obeid, A.M.: A comprehensive survey on wireless sensor node hardware platforms. Comput. Netw. 144, 89–110 (2018)

    Article  Google Scholar 

  8. Katiyar, S.K., Arun, P.: Comparative analysis of common edge detection techniques in context of object extraction. arXiv preprint arXiv:1405.6132 (2014)

  9. Ko, J.H.: Resource-aware and robust image processing for intelligent sensor systems. Ph.D. thesis, Georgia Institute of Technology (2018)

    Google Scholar 

  10. Ko, J.H., Mudassar, B.A., Mukhopadhyay, S.: An energy-efficient wireless video sensor node for moving object surveillance. IEEE Trans. Multi-Scale Comput. Syst. 1(1), 7–18 (2015). https://doi.org/10.1109/TMSCS.2015.2478469

    Article  Google Scholar 

  11. Kouadria, N., Mechouek, K., Harize, S., Doghmane, N.: Region-of-interest based image compression using the discrete Tchebichef transform in wireless visual sensor networks. Comput. Electr. Eng. 73, 194–208 (2019). https://doi.org/10.1016/j.compeleceng.2018.11.010

    Article  Google Scholar 

  12. Min, D., Choi, S., Lu, J., Ham, B., Sohn, K., Do, M.N.: Fast global image smoothing based on weighted least squares. IEEE Trans. Image Process. 23(12), 5638–5653 (2014)

    Article  MathSciNet  Google Scholar 

  13. Nguyen, H.A., Förster, A., Puccinelli, D., Giordano, S.: Sensor node lifetime: an experimental study. In: 2011 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops), pp. 202–207. IEEE (2011)

    Google Scholar 

  14. Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)

    Article  Google Scholar 

  15. Redondi, A., Baroffio, L., Bianchi, L., Cesana, M., Tagliasacchi, M.: Compress-then-analyze versus analyze-then-compress: what is best in visual sensor networks? IEEE Trans. Mob. Comput. 15(12), 3000–3013 (2016)

    Article  Google Scholar 

  16. Redondi, A., Baroffio, L., Cesana, M., Tagliasacchi, M.: Compress-then-analyze vs. analyze-then-compress: two paradigms for image analysis in visual sensor networks. In: 2013 IEEE 15th International Workshop on Multimedia Signal Processing (MMSP), pp. 278–282. IEEE (2013)

    Google Scholar 

  17. Rehman, U., Tariq, M., Sato, T.: A novel energy efficient object detection and image transmission approach for wireless multimedia sensor networks. IEEE Sens. J. 16(15), 5942–5949 (2016). https://doi.org/10.1109/JSEN.2016.2574989

    Article  Google Scholar 

  18. Rong, W., Li, Z., Zhang, W., Sun, L.: An improved canny edge detection algorithm. In: 2014 IEEE International Conference on Mechatronics and Automation, pp. 577–582. IEEE (2014)

    Google Scholar 

  19. Sarif, B.A., Pourazad, M.T., Nasiopoulos, P., Leung, V.C.: Encoding and communication energy consumption trade-off in H. 264/AVC based video sensor network. In: 2013 IEEE 14th International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM), pp. 1–6. IEEE (2013)

    Google Scholar 

  20. Sengar, S.S., Mukhopadhyay, S.: A novel method for moving object detection based on block based frame differencing. In: 2016 3rd International Conference on Recent Advances in Information Technology (RAIT), pp. 467–472. IEEE (2016)

    Google Scholar 

  21. Sengar, S.S., Mukhopadhyay, S.: Moving object detection based on frame difference and w4. SIViP 11(7), 1357–1364 (2017)

    Article  Google Scholar 

  22. Silveira, B., et al.: Power-efficient sum of absolute differences hardware architecture using adder compressors for integer motion estimation design. IEEE Trans. Circuits Syst. I Regul. Pap. 64(12), 3126–3137 (2017)

    Article  MathSciNet  Google Scholar 

  23. Xue, H., Zhang, Y., Wei, Y.: Fast ROI-based HEVC coding for surveillance videos. In: 2016 19th International Symposium on Wireless Personal Multimedia Communications (WPMC), pp. 299–304. IEEE (2016)

    Google Scholar 

  24. Zuo, J., Jia, Z., Yang, J., Kasabov, N.: Moving object detection in video sequence images based on an improved visual background extraction algorithm. Multimed. Tools Appl. 79(39), 29663–29684 (2020)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Badji Mokhtar University, Annaba, Algeria

    Ahcen Aliouat, Nasreddine Kouadria & Saliha Harize

  2. CRAN Laboratory, Lorraine University, Nancy, France

    Moufida Maimour

Authors
  1. Ahcen Aliouat
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nasreddine Kouadria
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Saliha Harize
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Moufida Maimour
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ahcen Aliouat .

Editor information

Editors and Affiliations

  1. Department of Computer Science, Ecole Militaire Polytechnique, Algiers, Algeria

    Mustapha Reda Senouci

  2. Department of Computer Science, Ecole Militaire Polytechnique, Algiers, Algeria

    Said Yacine Boulahia

  3. Department of Computer Science, Ecole Militaire Polytechnique, Algiers, Algeria

    Mohamed Akrem Benatia

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Aliouat, A., Kouadria, N., Harize, S., Maimour, M. (2022). Multi-Threshold-Based Frame Segmentation for Content-Aware Video Coding in WMSN. In: Senouci, M.R., Boulahia, S.Y., Benatia, M.A. (eds) Advances in Computing Systems and Applications. CSA 2022. Lecture Notes in Networks and Systems, vol 513. Springer, Cham. https://doi.org/10.1007/978-3-031-12097-8_29

Download citation

Publish with us

Policies and ethics

Search

Navigation