Skip to main content

Advertisement

Springer Nature Link
Log in

Real-time object detection on CUDA

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

Abstract

The aim of the research described in this article is to accelerate object detection in images and video sequences using graphics processors. It includes algorithmic modifications and adjustments of existing detectors, constructing variants of efficient implementations and evaluation comparing with efficient implementations on the CPUs. This article focuses on detection by statistical classifiers based on boosting. The implementation and the necessary algorithmic alterations are described, followed by experimental measurements of the created object detector and discussion of the results. The final solution outperforms the reference efficient CPU/SSE implementation, by approximately 6–8× for high-resolution videos using nVidia GeForce 9800GTX and Intel Core2 Duo E8200.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

Explore related subjects

Discover the latest articles and news from researchers in related subjects, suggested using machine learning.

References

  1. Bourdev, L., Brandt, J.: Robust object detection via soft cascade. In: CVPR (2005)

  2. Charles Brubaker, S., Mullin, M.D., Rehg, J.M.: Towards optimal training of cascaded detectors. In: ECCV06, pp. 325–337 (2006)

  3. Cha, Z., Viola, P.: Multiple-instance pruning for learning efficient cascade detectors. In: NIPS (2007)

  4. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR ’05: Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893, Washington, DC, USA, 2005. IEEE Computer Society

  5. Demirkir, C., Sankur, B.: Face detection using look-up table based gentle AdaBoost. In: Audio- and Video-based Biometric Person Authentication 2005, p. 339 (2005)

  6. Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. In: EuroCOLT ’95: Proceedings of the Second European Conference on Computational Learning Theory, pp. 23–37. Springer, London, UK (1995)

  7. Friedman, J., Hastie, T., Tibshirani, R.: Additive logistic regression: a statistical view of boosting. Ann. Stat. 28(2):337–407 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  8. Herout, A., Jošth, R., Zemčik, P., Hradiš, M.: GP-GPU implementation of the “Local Rank Differences” image feature. In: Proceedings of International Conference on Computer Vision and Graphics 2008, Lecture Notes in Computer Science, pp. 380–390. Springer, Verlag (2008)

  9. Herout, A., Zemčík, P., Hradiš, M., Juránek, R., Havel, J., Jošth, R., Žádník, M.: Pattern recognition, recent advances, chapter low-level image features for real-time object detection, p. 25. IN-TECH Education and Publishing (2009)

  10. Herout, A., Zemčík, P., Juránek, R., Hradiš, M.: Implementation of the “Local Rank Differences” image feature using SIMD instructions of CPU. In: Proceedings of Sixth Indian Conference on Computer Vision, Graphics and Image Processing. IEEE Computer Society (2008)

  11. Hou, C., Ai, H.Z., Lao, S.H.: Multiview pedestrian detection based on vector boosting. In: Proceedings of the 8th Asian Conference on Computer Vision, Part I. Tokyo, Japan, 18–22 November (2007)

  12. Hradiš, M., Herout, A., Zemčík, P.: Local rank patterns—novel features for rapid object detection. In: Proceedings of International Conference on Computer Vision and Graphics 2008, Number 12 in Lecture Notes in Computer Science, pp. 239–248. Springer (2008)

  13. Huang, C., Ai, H.Z., Li, Y., Lao, S.H.: High-performance rotation invariant multiview face detection. PAMI 29(4), 671–686 (2007)

    Article  Google Scholar 

  14. Li, S.Z., Zhu, L., Zhang, Z.Q., Blake, A., Zhang, H.J., Shum, H.: Statistical learning of multi-view face detection. In: ECCV ’02: Proceedings of the 7th European Conference on Computer Vision Part IV, pp. 67–81. Springer, London, UK (2002)

  15. Lienhart, R., Maydt, J.: An extended set of haar-like features for rapid object detection. In: IEEE ICIP 2002, pp. 900–903 (2002)

  16. Schapire, R.E., Singer, Y.: Improved boosting algorithms using confidence-rated predictions. Mach. Learn. 37, 297–336 (1999)

    Article  MATH  Google Scholar 

  17. Sochman, J., Matas, J.: Adaboost with totally corrective updates for fast face detection. In: AFGR04, pp. 445–450 (2004)

  18. Šochman, J., Matas, J.: WaldBoost—learning for time constrained sequential detection. In: CVPR ’05: Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 2, 20–26 June (2005)

  19. Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, p. 511 (2001)

  20. Zhang, L., Chu, R., Xiang, S., Liao, S.C., Li, S.Z.: Face detection based on multi-block LBP representation. In: ICB, pp. 11–18 (2007)

Download references

Acknowledgments

This work was supported by the Ministry of Education, Youth and Sports of the Czech Republic under the research program LC-06008 (Center for Computer Graphics) and by the research project “Security-Oriented Research in Information Technology” CEZMSMT, MSM0021630528.

Author information

Authors and Affiliations

  1. Graph@FIT, Brno University of Technology, Bozetechova 2, 616 00, Brno, Czech Republic

    Adam Herout, Radovan Jošth, Roman Juránek, Jiří Havel, Michal Hradiš & Pavel Zemčík

Authors
  1. Adam Herout
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Radovan Jošth
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Roman Juránek
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Jiří Havel
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Michal Hradiš
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Pavel Zemčík
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Adam Herout.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Herout, A., Jošth, R., Juránek, R. et al. Real-time object detection on CUDA. J Real-Time Image Proc 6, 159–170 (2011). https://doi.org/10.1007/s11554-010-0179-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11554-010-0179-0

Keywords

Profiles

  1. Adam Herout View author profile