ICCVG 2010: Computer Vision and Graphics pp 19-26 | Cite as
Localisation and Tracking of an Airport’s Approach Lighting System
Abstract
In this paper, we develop novel methods for extracting and tracking regions of interest from a given set of images. In particular, it is our aim to extract information about luminaires making up an airport landing lighting pattern in order to assess their performance. Initially to localise the luminaires we utilise sub pixel information to accurately locate the luminaire edges. Once the luminaires are located within the data, they are then tracked. We propose a new tracking algorithm based on control points and building blocks. Tests performed on a set of 422 images taken during an approach to an airport in Northern Ireland have shown that when combined the localisation and tracking techniques are very effective when compared to standard techniques (KLT and SIFT) as well as to model based matching technique for this application.
Keywords
Photometrics vibration luminaire localisation trackingPreview
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References
- 1.Chowdhury, S., McMenemy, K., Peng, J.: Performance evaluation of airport lighting using mobile camera techniques. In: Jiang, X., Petkov, N. (eds.) CAIP 2009. LNCS, vol. 5702, pp. 1171–1178. Springer, Heidelberg (2009)CrossRefGoogle Scholar
- 2.Liu, J.: Robust image segmentation using local median. In: Proc. of the 3rd Canadian Conf. on Computer and Robot Vision, CRV 2006. IEEE Computer Society Press, Los Alamitos (2006)Google Scholar
- 3.Nhat, V., Manjunath, B.: Shape prior segmentation of multiple objects with graph cuts. In: Proc. of IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008, June 23-28, pp. 1–8 (2008)Google Scholar
- 4.Niblock, J., Peng, J., McMenemy, K., Irwin, G.: Fast model-based feature matching technique applied to airport lighting. IET Science, Measurement and Technology 2(3), 160–176 (2008)CrossRefGoogle Scholar
- 5.Niblock, J., McMenemy, K., Ferguson, S., Peng, J.: Autonomous tracking system for airport lighting quality control. In: 2nd International Conference on Computer Vision Theory and Applications, March 8-11, vol. (2), pp. 317–324 (2007)Google Scholar
- 6.Niblock, J., Peng, J., McMenemy, K., Irwin, G.: Autonomous model-based object identification and camera position estimation with application to airport lighting quality control. In: Proc. of 3rd Int. Conference on Computer Vision Theory and Applications, VISAPP, Funchal, Portugal, January 12-15, vol. (2) (2008)Google Scholar
- 7.Okada, K., Akdemir, U.: Blob segmentation using joint space-intensity likelihood ratio test: application to 3d tumor segmentation. In: IEEE Conf. on Comp. Vision and Pattern Recognition. CVPR 2005, June 20-25, vol. 2, pp. 437–444 (2005)Google Scholar
- 8.Organization, I.C.A.: Aerodrome design and operations, annex 14, 4th edn., vol. 14 (July 2004)Google Scholar
- 9.Saha, S., Das, A., Chanda, B.: An automatic image segmentation technique based on pseudo-convex hull. In: Proc. of Indian Conference on Computer Vision, Graphics and Image Processing, ICCVGI 2006 (2006)Google Scholar
- 10.Wong, W., Chung, A.: Bayesian image segmentation using local iso-intensity structural orientation. IEEE Transaction on Image Processing 14(10), 1512–1523 (2005)CrossRefGoogle Scholar