Skip to main content
SpringerLink
Log in

ATDT: Autonomous Template-Based Detection and Tracking of Objects from Airborne Camera

  • Conference paper
Book cover Intelligent Systems'2014

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 323))

Abstract

This paper describes the proposed ATDT approach for autonomous template-based detection and tracking from a moving airborne camera or an electro-optical (EO) device. The advantages of the proposed method ATDT is that it is fully autonomous (no human involvement is needed; in the experiment the human was only involved to navigate the UAV not for any of the steps of the video analysis) replacing the need of human operators for video analytics tasks, it can be used on board UAV, is computationally lean and can operate in real time.

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 169.00
Price excludes VAT (USA)
  • Available as 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

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. Bay, H., et al.: SURF: Speeded Up Robust Features. In: Computer Vision and Image Understanding (CVIU), pp. 346–359 (2008)

    Google Scholar 

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

    Article  Google Scholar 

  3. Barron, J.L., Fleet, D.J., Beauchemin, S.: Performance of optical flow techniques. International Journal of Computer Vision 12(1), 43–77 (1994)

    Article  Google Scholar 

  4. Beauchemin, S.S., Barron, J.L.: The computation if optical flow. Journal of Computing Surveys (CSUR) 27(3), 433–466 (1995)

    Article  Google Scholar 

  5. Elgammal, A., et al.: Background and Foreground Modeling using Nonparametric Kernel Density for Visual Surveillance. Proc. of the IEEE (2002)

    Google Scholar 

  6. Sadeghi-Tehran, P., Angelov, P.: ARTOD: Autonomous Real Time Objects Detection by a Moving Camera using Recursive Density Estimation. In: Novel Applications of Intelligent Systems (2014)

    Google Scholar 

  7. Prat, W.K.: Correlation techniques of image registration. IEEE Trans. on Aerospace and Electronic Systems 10, 353–358 (1974)

    Article  Google Scholar 

  8. Onoe, M., Saito, M.: Automatic threshold setting for the sequential similarity detection algorithm. IEEE Trans. on Comput., 1052–1053 (1976)

    Google Scholar 

  9. Szeliski, R.: Computer Vision: Algorithms and Applications. Springer (2010)

    Google Scholar 

  10. Tuytelaars, T., Mikolajczyk, K.: Local invariant feature detectors. Foundations and Trends in Computer Graphics and Computer Vision 3(1) (2007)

    Google Scholar 

  11. Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(10), 1615–1630 (2005)

    Article  Google Scholar 

  12. Triggs, B.: Detecting keypoints with stable position, orientation, and scale under illumination changes. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3024, pp. 100–113. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  13. Schmid, C., Mohr, R., Bauckhage, C.: Evaluation of interest point detectors. International Journal of Computer Vision 37(2), 151–172 (2000)

    Article  MATH  Google Scholar 

  14. Brown, M., Lowe, D.: Automatic panoramic image stitching using invariant features. International Journal of Computer Vision 74(1), 59–73 (2007)

    Article  Google Scholar 

  15. Fergus, R., Perona, P., Zisserman, A.: Weakly supervised scale-invariant learning of models for visual recognition. International Journal of Computer Vision 71(3), 273–303 (2007)

    Article  Google Scholar 

  16. Funayama, R., et al.: Robust Interest Point Detector and Descriptor, US (2009)

    Google Scholar 

  17. Lowe, D.: Method and apparatus for identifying scale invariant features in an image and use of same for locating an object in an image (2004)

    Google Scholar 

  18. Rosten, E., Porter, R., Drummond, T.: Faster and better: a machine learning approach to corner detection. IEEE Trans. Pattern Analysis and Machine Intelligence 32, 105–119 (2010)

    Article  Google Scholar 

  19. Harris, C., Stephens, M.: A combined corner and edge detector. In: Proceedings of the 4th Alvey Vision Conference (1988)

    Google Scholar 

  20. Calonder, M., et al.: BRIEF: Binary Robust Independent Elementary Features. IEEE Transactions on Pattern Analysis and Machine Intelligence (2012)

    Google Scholar 

  21. Leutenegger, S., Chli, M., Siegwart, R.Y.: BRISK: Binary Robust Invariant Scal-able Keypoints. In: IEEE International Conference on Computer Vision (ICCV), pp. 2548–2555 (2011)

    Google Scholar 

  22. Khvedchenia, I.: A battle of three descriptors: SURF, FREAK and BRISK (2012) (cited November 1, 2012)

    Google Scholar 

  23. GAMMA: Growing Autonomous Mission MAnagement systems Programme. funded by the Regional Growth Fund, BIS (2012-2014)

    Google Scholar 

  24. Mair, E., Hager, G.D., Burschka, D., Suppa, M., Hirzinger, G.: Adaptive and generic corner detection based on the accelerated segment test. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part II. LNCS, vol. 6312, pp. 183–196. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  25. Laganiere, R.: Opencv 2 Computer Vision Application Programming Cookbook. Packt Publishing Ltd., UK (2011)

    Google Scholar 

  26. Fischler, M.A., Bolles, R.C.: Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Comm. of the ACM 24(6), 381–395 (1981)

    Article  MathSciNet  Google Scholar 

  27. Hartley, R., Zisserman, A.: Multiple view geometry in computer vision. Cambridge Univ. Pr. (2003)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computing and Communications, Lancaster University, Lancaster, United Kingdom

    Pouria Sadeghi-Tehran & Plamen Angelov

Authors
  1. Pouria Sadeghi-Tehran
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Plamen Angelov
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pouria Sadeghi-Tehran .

Editor information

Editors and Affiliations

  1. Ford Motor Company, Research & Advanced Engineering, Dearborn, Mississippi, USA

    D. Filev

  2. Industrial Research Institute for Automation and Measurements (PIAP), Warsaw, Poland

    J. Jabłkowski

  3. Polish Academy of Sciences, Systems Research Institute, Warsaw, Poland

    J. Kacprzyk

  4. Polish Academy of Sciences and WIT - Warsaw School of Information Technology, Systems Research Institute, Warsaw, Poland

    M. Krawczak

  5. Bulgarian Academy of Sciences, Institute of Information and Communication, Sofia, Bulgaria

    I. Popchev

  6. Department of Computer Engineering, Częstochowa University of Technology, Częstochowa, Poland

    L. Rutkowski

  7. Bulgarian Academy of Sciences, Institute of Information and Communication Technologies, Sofia, Bulgaria

    V. Sgurev

  8. Department of Computer and Information Technologies, “Prof. Assen Zlatarov" University Faculty of Technical Sciences, Bourgas, Bulgaria

    E. Sotirova

  9. Industrial Research Institute for Automation and Measurements (PIAP), Warsaw, Poland

    P. Szynkarczyk

  10. Polish Academy of Sciences, Systems Research Institute, Warsaw, Poland

    S. Zadrozny

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Sadeghi-Tehran, P., Angelov, P. (2015). ATDT: Autonomous Template-Based Detection and Tracking of Objects from Airborne Camera. In: Filev, D., et al. Intelligent Systems'2014. Advances in Intelligent Systems and Computing, vol 323. Springer, Cham. https://doi.org/10.1007/978-3-319-11310-4_48

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-11310-4_48

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11309-8

  • Online ISBN: 978-3-319-11310-4

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation