Skip to main content

The Use of HACP+SBT Lossless Compression in Optimizing Memory Bandwidth Requirement for Hardware Implementation of Background Modelling Algorithms

  • Conference paper
  • First Online:
Applied Reconfigurable Computing. Architectures, Tools, and Applications (ARC 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10824))

Included in the following conference series:

Abstract

In this paper the issue of optimizing memory bandwidth to external RAM in FPGA hardware implementation of foreground object segmentation methods is discussed. Three representative background modelling algorithms: Running Average (RA), Gaussian Mixture Model (GMM) and Pixel Based Adaptive Segmenter (PBAS) and three lossless compression methods: Run Length Encoding (RLE), Huffman coding and Hierarchical Average and Copy Prediction (HACP) with Significant Bit Truncation (SBT) coding were considered. After initial simulations in a software model, it was decided to implement the HACP+SBT approach in hardware. In addition, the possibility of using the proposed solution for ultra high-definition video stream processing was evaluated.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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

References

  1. Genovese, M., Napoli, E.: ASIC and FPGA implementation of the Gaussian mixture model algorithm for real-time segmentation of high definition video. IEEE Trans. Very Large Scale Integr. VLSI Syst. 22(3), 537–547 (2014)

    Article  Google Scholar 

  2. Hoffman, M., Tiefenbacher, P., Rigoll, G.: Background segmentation with feedback: the pixel-based adaptive segmenter. In: 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 35–47 (2012)

    Google Scholar 

  3. Jiang, H., Öwall, V., Ardo, H.: Real-time video segmentation with VGA resolution and memory bandwidth reduction. In: International Conference on Video and Signal Based Surveillance, p. 104 (2006)

    Google Scholar 

  4. Jiang, H., Ardo, H., Owall, V.: A hardware architecture for real-time video segmentation utilizing memory reduction techniques. IEEE Trans. Circuits Syst. Video Technol. 19(2), 226–236 (2009)

    Article  Google Scholar 

  5. Kristensen, F., Hedberg, H., Jiang, H., Nilsson, P., Öwall, V.: An embedded real-time surveillance system: implementation and evaluation. J. Sig. Process. Syst. 52(1), 75–94 (2008)

    Article  Google Scholar 

  6. Kryjak, T., Komorkiewicz, M., Gorgon, M.: Hardware implementation of the PBAS foreground detection method in FPGA. In: 2013 Proceedings of the 20th International Conference Mixed Design of Integrated Circuits and Systems (MIXDES), pp. 479–484 (2013)

    Google Scholar 

  7. Kim, J., Kim, J., Kyung, C.: A lossless embedded compression algorithm for high definition video coding. In: 2009 IEEE International Conference on Multimedia and Expo, pp. 193–196 (2009)

    Google Scholar 

  8. Kim, J., Kyung, C.: A lossless embedded compression using significant bit truncation for HD video coding. IEEE Trans. Circuits Syst. Video Technol. 20(6), 848–860 (2010)

    Article  Google Scholar 

  9. Salomon, D., Motta, G.: Handbook of Data Compression. Springer, London (2010). https://doi.org/10.1007/978-1-84882-903-9

    Book  MATH  Google Scholar 

  10. Stauffer, C., Grimson, W.E.L.: Adaptive background mixture models for real-time tracking. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 246–252 (1999)

    Google Scholar 

  11. Wang, Y., Jodoin, P., Konrad, J., Benezeth, Y., Ishwar, P.: CDnet 2014: an expanded change detection benchmark dataset. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 393–400 (2014)

    Google Scholar 

  12. Wren, C.R., Azarbayejani, A., Darrell, T., Pentland, A.P.: Pfinder: real-time tracking of the human body. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 780–785 (1997)

    Article  Google Scholar 

Download references

Acknowledgements

The work presented in this paper was supported by the National Science Centre project no. 2016/23/D/ST6/01389.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Tomasz Kryjak .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Piszczek, K., Janus, P., Kryjak, T. (2018). The Use of HACP+SBT Lossless Compression in Optimizing Memory Bandwidth Requirement for Hardware Implementation of Background Modelling Algorithms. In: Voros, N., Huebner, M., Keramidas, G., Goehringer, D., Antonopoulos, C., Diniz, P. (eds) Applied Reconfigurable Computing. Architectures, Tools, and Applications. ARC 2018. Lecture Notes in Computer Science(), vol 10824. Springer, Cham. https://doi.org/10.1007/978-3-319-78890-6_31

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-78890-6_31

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-78889-0

  • Online ISBN: 978-3-319-78890-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics