Skip to main content
SpringerLink
Log in

A New Approach of GPU Accelerated Visual Tracking

  • Conference paper
Book cover Advanced Concepts for Intelligent Vision Systems (ACIVS 2010)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 6475))

Abstract

In this paper a fast and robust visual tracking approach based on GPU acceleration is proposed. It is an effective combination of two GPU-accelerated algorithms. One is a GPU accelerated visual tracking algorithm based on the Efficient Second-order Minimization (GPU-ESM) algorithm. The other is a GPU based Scale Invariant Feature Transform (SIFT) algorithm, which is used in those extreme cases for GPU-ESM tracking algorithm, i.e. large image differences, occlusions etc. System performances have been greatly improved by our combination approach. We have extended the tracking region from a planar region to a 3D region. Translation details of both GPU algorithms and their combination strategy are described. System performances are evaluated with experimental data. Optimization techniques are presented as a reference for GPU application developers.

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 39.99
Price excludes VAT (USA)
  • Available as 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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Szeliski, R.: Handbook of Mathematical Models in Computer Vision, pp. 273–292. Springer, Heidelberg (2006)

    Book  Google Scholar 

  2. Baker, S., Matthews, I.: Lucas-Kanade 20 Years On: A Unifying Framework. International Journal of Computer Vision 56(3), 221–255 (2004)

    Article  Google Scholar 

  3. Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision, pp. 11–23. Cambridge University Press, Cambridge (2000)

    MATH  Google Scholar 

  4. Lucas, B.D., Kanade, T.: An Iterative Image Registration Technique with an Application to Stereo Vision. In: Proceedings of the International Joint Conference on Artificial Intelligence, pp. 674–679 (1981)

    Google Scholar 

  5. Tomasi, C., Kanade, T.: Detection and Tracking of Point Features., Carnegie Mellon University Technical Report CMU-CS-91-132 (April 1991)

    Google Scholar 

  6. Benhimane, S., Malis, E.: Real-time image-based tracking of planes using efficient second-order minimization. In: IEEE/RSJ International Conference on Intelligent Robots Systems, Sendai, Japan (2004)

    Google Scholar 

  7. Malis, E.: Improving vision-based control using efficient second-order minimization techniques. In: IEEE International Conference on Robotics and Automation, New Orleans, USA (April 2004)

    Google Scholar 

  8. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)

    Article  Google Scholar 

  9. Zang, C., Hashimoto, K.: GPU acceleration in a Visual Servo System. In: ICAM 2010, Osaka, Japan (October 2010)

    Google Scholar 

  10. Malis, E.: An efficient unified approach to direct visual tracking of rigid and deformable surfaces. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, San Diego, USA (October 2007)

    Google Scholar 

  11. Lowe, David, G.: Object recognition from local scale-invariant features. In: Proceedings of the International Conference on Computer Vision, vol. 2, pp. 1150–1157 (1999)

    Google Scholar 

  12. Morel, J.M., Yu, G.: ASIFT: A New Framework for Fully Affine Invariant Image Comparison. SIAM Journal on Imaging Sciences 2(2), 438–469 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  13. Ke, Y., Sukthankar, R.: PCA-SIFT: A more distinctive representation for local image descriptors. In: Proceeding of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 506–513 (2004)

    Google Scholar 

  14. Bay, H., Tuytelaars, Y., Van, G.L.: Speeded-Up Robust Features (SURF). Computer Vision and Image Understanding 110, 346–359 (2008)

    Article  Google Scholar 

  15. Fung, J., Mann, S.: OpenVIDIA: parallel GPU computer vision. ACM MULTIMEDIA, 849–852 (2005)

    Google Scholar 

  16. Fung, J., Mann, S.: Computer Vision Signal Processing on Graphics Processing Units. In: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. V-93–V-96 (2004)

    Google Scholar 

  17. Sinha, S., Frahm, J.-M., Pollefeys, M., Genc, Y.: Feature tracking and matching in video using programmable graphics hardware. Machine Vision and Applications (March 2007)

    Google Scholar 

  18. Heymann, S., Muller, K., Smolic, A., Froehlich, B., Wiegand, T.: SIFT implementation and optimization for general-purpose GPU. In: WSCG 2007, pp. 317–322 (2007)

    Google Scholar 

  19. http://www.cs.unc.edu/~ccwu/siftgpu/

Download references

Author information

Authors and Affiliations

  1. Graduate School of Information Sciences, Tohoku University, 6-6-01 Aramaki Aza Aoba, Aoba-ku, Sendai, 980-8579, Japan

    Chuantao Zang & Koichi Hashimoto

Authors
  1. Chuantao Zang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Koichi Hashimoto
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. DGA/D4S/MRIS, CEP/GIP, 16 bis avenue Prieur de la côte d’or, 94114, Arcueil, France

    Jacques Blanc-Talon

  2. Canon Information Systems Research Australia, Sydney, Australia

    Don Bone

  3. Telecommunications and Information processing (TELIN), Ghent University, St.-Pietersnieuwstraat 41, B9000, Ghent, Belgium

    Wilfried Philips

  4. CSIRO ICT Centre, ICT Centre, Epping, NSW 1710, Po Box 76, 1710, Sydney, Australia

    Dan Popescu

  5. Physics, University of Antwerp, Universiteitsplein 1; Building N., 2610, Wilrijk, Belgium

    Paul Scheunders

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Zang, C., Hashimoto, K. (2010). A New Approach of GPU Accelerated Visual Tracking. In: Blanc-Talon, J., Bone, D., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2010. Lecture Notes in Computer Science, vol 6475. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17691-3_33

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-17691-3_33

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-17690-6

  • Online ISBN: 978-3-642-17691-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation