AUTONOMOUS MODEL-BASED OBJECT IDENTIFICATION & CAMERA POSITION ESTIMATION WITH APPLICATION TO AIRPORT LIGHTING QUALITY CONTROL

James H. Niblock, Jian-Xun Peng, Karen R. McMenemy, George W. Irwin

2008

Abstract

The development of an autonomous system for the accurate measurement of the quality of aerodrome ground lighting (AGL) in accordance with current standards and recommendations is presented. The system is composed of an imager which is placed inside the cockpit of an aircraft to record images of the AGL during a normal descent to an aerodrome. Before the performance of the AGL is assessed, it is first necessary to uniquely identify each luminaire within the image and track it through the complete image sequence. A model-based (MB) methodology is used to ascertain the optimum match between a template of the AGL and the actual image data. Projective geometry, in addition to the image and real world location of the extracted luminaires, is then used to calculate the position of the camera at the instant the image was acquired. Algorithms are also presented which model the distortion apparent within the sensors optical system and average the camera’s intrinsic parameters over multiple frames, so as to minimise the effects of noise on the acquired image data and hence make the camera’s estimated position and orientation more accurate. The positional information is validated using actual approach image data.

References

  1. Chatterji, G., Menon, P., and Sridhar, B. (1998). Visionbased position and attitude determination for aircraft night landing. Journal of Guidance, Control, and Dynamics, 21(1).
  2. Faugeras, O. and Toscani, G. (1987). Camera calibration for 3d computer vision. Proc. Int'l Workshop Industrial Applications of Machine Vision and Machine Intelligence, pages 240-247.
  3. Heikkila, J. (2000). Geometric camera calibration using circular control points. IEEE Trans. PAMI, 22(10).
  4. Horonjeff, R. and McKelvey, F. (1993). Planning and Design of Airports. McGraw Hill, 4th ed. edition.
  5. ICAO (2004). Aerodrome Design and Operations. International Civil Aviation Organization, 4th edition.
  6. McMenemy, K. (2003). Photometric Evaluation of Aerodrome Ground Lighting. PhD thesis, Queen's University Belfast.
  7. Milward, R. (1976). New approach to airport lighting inspection. Shell Aviation News, 437:26-31.
  8. Mostafavi, H. and Malone, M. (1995). Landing trajectory measurement using onboard video sensor and runway landmarks. Proceedings of SPIE - The International Society for Optical Engineering, 2463:116-127.
  9. Niblock, J., Peng, J., McMenemy, K., and Irwin, G. (2007a). Autonomous tracking system for airport lighting quality control. Proceedings of the 2nd International Conference on Computer Vision Theory and Applications, VISAPP, Motion Tracking and Stereo Vision:317-324.
  10. Niblock, J., Peng, J., McMenemy, K., and Irwin, G. (2007b). Fast model-based feature matching technique applied to airport lighting. Transactions of the IET Science, Measurement & Technology, In Press.
  11. Peng, J., Li, K., and Huang, D. (2006). A hybrid forward algorithm for rbf neural network construction. IEEE Transactions on Neural Networks, 17(6):1439-1451.
  12. Sinha, S., Frahm, J., Pollefeys, M., and Genc, Y. (2006). Gpu-based video feature tracking and matching. Technical report 06-012, UNC Chapel Hill Department of Computer Science.
  13. Soni, T. and Sridhar, B. (1994). Modelling issues in vision based aircraft navigation during landing. IEEE Workshop on Applications of Computer Vision Proceedings, pages 89-96.
  14. Sridhar, B., Chatterji, G., and T.Soni (1996). Model-based vision for aircraft position determination. Control Engineering Practice, 4(8):1153-1159.
  15. V. Lepetit, P. F. (2005). Monocular model-based 3d tracking of rigid objects: A survey. Foundations and Trends in Computer Graphics and Vision, 1(1):1-89.
  16. Vincent, E. and Laganiere, R. (2001). Detecting planar homographies in an image pair. IEEE Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis, pages 182-187.
Download


Paper Citation


in Harvard Style

H. Niblock J., Peng J., R. McMenemy K. and W. Irwin G. (2008). AUTONOMOUS MODEL-BASED OBJECT IDENTIFICATION & CAMERA POSITION ESTIMATION WITH APPLICATION TO AIRPORT LIGHTING QUALITY CONTROL . In Proceedings of the Third International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2008) ISBN 978-989-8111-21-0, pages 383-390. DOI: 10.5220/0001085503830390


in Bibtex Style

@conference{visapp08,
author={James H. Niblock and Jian-Xun Peng and Karen R. McMenemy and George W. Irwin},
title={AUTONOMOUS MODEL-BASED OBJECT IDENTIFICATION & CAMERA POSITION ESTIMATION WITH APPLICATION TO AIRPORT LIGHTING QUALITY CONTROL},
booktitle={Proceedings of the Third International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2008)},
year={2008},
pages={383-390},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001085503830390},
isbn={978-989-8111-21-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Third International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2008)
TI - AUTONOMOUS MODEL-BASED OBJECT IDENTIFICATION & CAMERA POSITION ESTIMATION WITH APPLICATION TO AIRPORT LIGHTING QUALITY CONTROL
SN - 978-989-8111-21-0
AU - H. Niblock J.
AU - Peng J.
AU - R. McMenemy K.
AU - W. Irwin G.
PY - 2008
SP - 383
EP - 390
DO - 10.5220/0001085503830390