Skip to main content
SpringerLink
Log in

Real-Time Object Tracking on iPhone

  • Conference paper
Advances in Visual Computing (ISVC 2011)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 6938))

Included in the following conference series:

Abstract

A novel real-time object tracking algorithm is proposed which tracks objects in real-time on an iPhone platform. The system utilizes information such as image intensity, color, edges, and texture for matching different candidate tracks. The tracking system adapts to changes in target appearance and size (including resizing candidate tracks to a universal depth-independent size) while running at 10-15FPS tracking rate. Several experiments conducted on actual video are used to illustrate the proposed approach.

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. Yilmaz, A., Javed, O., Shah, M.: Object tracking: A survey. ACM Computing Surveys 38 (2006)

    Google Scholar 

  2. Comaniciu, D., Ramesh, V., Meer, P.: Kernel-based object tracking. IEEE Trans. on Pattern analysis and Machine Intelligence 25, 564–577 (2004)

    Article  Google Scholar 

  3. Shi, J., Tomasi, C.: Good features to track. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 593–600 (1994)

    Google Scholar 

  4. Tao, H., Sawhney, H., Kumar, R.: Object tracking with bayesian estimation of dynamic layer representations. IEEE Trans. on Pattern analysis and Machine Intelligence 24, 75–89 (2002)

    Article  Google Scholar 

  5. Comaniciu, D., Ramesh, V., Meer, P.: Real-time tracking of non-rigid objects using mean shift. In: IEEE CVPR (2000)

    Google Scholar 

  6. Nummiaro, K., Koller-Meier, E., Gool, L.: Color features for tracking non-rigid objects. Special Issue on Visual Surveillance. Chinese Journal of Automation (2003)

    Google Scholar 

  7. Scharcanski, J., Venetsanopoulos, A.N.: Edge detection of color images using directional operators. IEEE Trans. on Circuits and Systems for Video Technology 7, 397–401 (1997)

    Article  Google Scholar 

  8. Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. IEEE Trans. on Pattern analysis and Machine Intelligence 24, 603–619 (2002)

    Article  Google Scholar 

  9. Wand, M.P., Jones, M.C.: Kernel Smoothing. Chapman and Hall, Boca Raton (1995)

    Book  MATH  Google Scholar 

  10. Bovik, A.C.: Handbook of image and video processing. Academic Press, London (2005)

    MATH  Google Scholar 

  11. Apple: Accelerate framework reference (2010), http://developer.apple.com/library/ios/#documentation/Accelerate/Reference/AccelerateFWRef/_index.html

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Toronto, Toronto, Canada

    Amin Heidari & Parham Aarabi

Authors
  1. Amin Heidari
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Parham Aarabi
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. University of Nevada, 89557, Reno, NV, USA

    George Bebis

  2. NASA Ames Research Center, 94035, Moffett Field, CA, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, 94720, Berkeley, CA, USA

    Bahram Parvin

  4. Desert Research Institute, 89512, Reno, NV, USA

    Darko Koracin

  5. Department of Computer Science and Engineering, University of South Carolina, 29208, Columbia, SC, USA

    Song Wang

  6. HRL Laboratories, 3011 Malibu Canyon Road, 90265-4797, Malibu, CA, USA

    Kim Kyungnam

  7. Purdue University, 47907-2021, West Lafayette, IN, USA

    Bedrich Benes

  8. Sandia National Laboratory, 87185, Albuquerque, NM, USA

    Kenneth Moreland

  9. University of Louisiana, 70504, Lafayette, LA, USA

    Christoph Borst

  10. Adobe Systems Incorporated, San Francisco, CA, USA

    Stephen DiVerdi

  11. Polytechnic Institute of NYU, 11201, Brooklyn, NY, USA

    Chiang Yi-Jen

  12. Lawrence Livermore National Laboratory, 94551-0808, Livermore, CA, USA

    Jiang Ming

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Heidari, A., Aarabi, P. (2011). Real-Time Object Tracking on iPhone. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2011. Lecture Notes in Computer Science, vol 6938. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24028-7_71

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-24028-7_71

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-24027-0

  • Online ISBN: 978-3-642-24028-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation