Skip to main content

Implementation of AdaBoost Face Detection Using Vivado HLS

  • Conference paper
  • First Online:
  • 2155 Accesses

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 516))

Abstract

For the problem that Adaptive Boosting (AdaBoost) face detection algorithm is slowly implemented on the embedded platform by software, this paper adopts the method of the full hardware acceleration. The intellectual property (IP) core of AdaBoost algorithm is designed by Vivado high-level synthesis (HLS), which may reduce the development difficulty and shorten the development cycle. The design adopts the serial–parallel structure to accelerate face detection and uses several methods of optimizing hardware resource. The face detection algorithm is implemented on the Zedboard platform and achieves the purpose of real-time detection.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   219.99
Price excludes VAT (USA)
  • Durable hardcover 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

Learn about institutional subscriptions

References

  1. Liang L, Ai H, Zhang B. A survey of human face detection. Chin J Comput. 2002;25(5):449–58.

    Google Scholar 

  2. Zhu M, Lu X, Lu H. Transplantation and optimization of AdaBoost face detection algorithm on DSP. Comput Eng Appl. 2014;50(20):197–201.

    Google Scholar 

  3. Zhang Z, Feng Z, Wei S. Implementation of face detection system based on AdaBoost algorithm on embedded system. Inf Technol. 2008;07:167–70.

    Google Scholar 

  4. Xilinx Inc. High-level-synthesis v17.1 [EB/OL]. URL https://www.xilinx.com/support/documentation/sw_manuals/xilinx2018_1/ug902-vivado-high-level-synthesis.pdf.

  5. Viola P, Jones M. Rapid object detection using a boosted cascade of simple features. In: Computer society conference on computer vision and pattern recognitionl, vol. 1; 2001. p. I-511–8.

    Google Scholar 

  6. Acasandrei L, Barriga A. Accelerating Viola-Jones face detection for embedded and SoC environments. In: ACM/IEEE international conference on distributed smart cameras, Ghent; 2011. p. 1–6.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Kejun Tan .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Liu, S., Tan, K., Yang, B. (2020). Implementation of AdaBoost Face Detection Using Vivado HLS. In: Liang, Q., Liu, X., Na, Z., Wang, W., Mu, J., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2018. Lecture Notes in Electrical Engineering, vol 516. Springer, Singapore. https://doi.org/10.1007/978-981-13-6504-1_115

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-6504-1_115

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-6503-4

  • Online ISBN: 978-981-13-6504-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics