Abstract
Feature tracking is an important task in computer vision, especially for 3D reconstruction applications. Such procedures can be run in environments with a controlled sensor, e.g. a robot arm with camera. This yields the camera parameters as special knowledge that should be used during all steps of the application to improve the results. As a first step, KLT (Kanade-Lucas-Tomasi) tracking (and its variants) is an approach widely accepted and used to track image point features. So, it is straightforward to adapt KLT tracking in a way that camera parameters are used to improve the feature tracking results. The contribution of this work is an explicit formulation of the KLT tracking procedure incorporating known camera parameters. Since practical applications do not run without noise, the uncertainty of the camera parameters is regarded and modeled within the procedure of Guided KLT tracking (GKLT). Comparing practical experiments have been performed and the results are presented.
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References
Baker, S., Matthews, I.: Lucas-kanade 20 years on: A unifying framework. International Journal of Computer Vision 56, 221–255 (2004)
Cox, I., Roy, S., Hingorani, S.L.: Dynamic histogram warping of image pairs for constant image brightness. In: IEEE International Conference on Image Processing, vol. 2, pp. 366–369 (1995)
Fusiello, A., Trucco, E., Tommasini, T., Roberto, V.: Improving feature tracking with robust statistics. Pattern Analysis and Applications 2, 312–320 (1999)
Hartley, R., Zisserman, A.: Multiple View Geometry in computer vision, Second edn. Cambridge University Press, Cambridge (2003)
Kuehmstedt, P., Notni, G., Hintersehr, J., Gerber, J.: Cad-cam-system for dental purpose – an industrial application. In: The 4th International Workshop on Automatic Processing of Fringe Patterns (2001)
Lucas, B., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: Proceedings of 7th International Joint Conference on Artificial Intelligence (1981)
Rav-Acha, A., Peleg, S.: Lucas-kanade without iterative warping. In: Proceedings of 2006 IEEE International Conference on Image Processing (2006)
Shi, J., Tomasi, C.: Good features to track. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (1994)
Trummer, M., Denzler, J., Munkelt, C.: Guided KLT – KLT Tracking for Controlled Environments Regarding Uncertainty. Technical report, Chair for Computer Vision, Friedrich-Schiller University of Jena (2008)
Trummer, M., Denzler, J., Suesse, H.: Precise 3d measurement with standard means and minimal user interaction – extended single-view reconstruction. In: Proceedings of 17th International Conference on the Application of Computer Science and Mathematics in Architecture and Civil Engineering (2006)
Wenhardt, S., Deutsch, B., Hornegger, J., Niemann, H., Denzler, J.: An information theoretic approach for next best view planning in 3-d reconstruction. In: The 18th International Conference on Pattern Recognition (2006)
Zinsser, T., Graessl, C., Niemann, H.: High-speed feature point tracking. In: Proceedings of Conference on Vision, Modeling and Visualization (2005)
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Trummer, M., Denzler, J., Munkelt, C. (2009). Guided KLT Tracking Using Camera Parameters in Consideration of Uncertainty. In: Ranchordas, A., Araújo, H.J., Pereira, J.M., Braz, J. (eds) Computer Vision and Computer Graphics. Theory and Applications. VISIGRAPP 2008. Communications in Computer and Information Science, vol 24. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10226-4_20
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DOI: https://doi.org/10.1007/978-3-642-10226-4_20
Publisher Name: Springer, Berlin, Heidelberg
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