Abstract
Vision-based control of motion can only be feasible if vision provides reliable control signals and when full system integration is achieved. In this paper we will address these two issues. A modular system architecture is built up around the basic primitives of object tracking, the features of the object. The initialisation is partly automated by using search functions to describe the task. The features found and tracked in the image are contained in a wire-frame model of the object as seen in the image. This model is used for feature tracking and continuous pose determination. Of particular need is a method of robust feature tracking. This is achieved using EPIC, a method of Edge-Projected Integration of Cues. A demonstration shows how the robot follows the pose of an object moved by hand in common room lighting at frame rate using a PC.
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References
R. Bajcsy, S.W. Lee, and A. Leonardis: Detection of diffuse and specular interface reflactions and inter-reflections by color image segmentation; Int. J. of Computer Vision 17, pp. 241–272, 1996.
Christensen, H.I., Vieville, T.: System Design and Control; ECVNet webpage, http://pandora.inrialpes.fr/ECVNet/Policy/System.Design.Control.html#RTF ToC4, 1996.
Cox, I.J., Hingorani, S.L.: An Efficient Implementation of Reid’s Multiple Hypothesis Tracking Algorithm and Its Evaluation for the Purpose of Visual Tracking; IEEE Trans. PAMI Vol. 18(2), 1996, S. 138–150.
Corke, P.I., Good, M.C.: Dynamic Effects in Visual Closed-Loop Systems; IEEE Trans. on RA Vol. 12(5), pp. 671–683, 1996.
Crowley, J.L., Christensen, H.I.: Vision as Process; Springer Verlag, 1995.
Dickmanns, D.E.: The 4D-Approach to Dynamic Machine Vision; Conf. on Decision and Control, pp. 3770–3775, 1994.
Fischler, M.A., Bolles, R.C.: Random Sample Consensus: A Paradigm for Model Fitting; Communications of the ACM Vol. 24(6), pp. 381–395, 1981.
Förstner, W., Gülch, E.: A Fast Operator for Detection and Precise Location of Distinct Points, Corners and Centers of Circular Features; ISPRS Intercommission Workshop, Interlaken, 1987.
Gamble, E.B., Geiger, D., Poggio, T., Weinshall, D.: Integration of Vision Modules and Labeling of Surface Discontinuities; IEEE SMC Vol. 19(6), pp. 1576–1581, 1989.
Gee, A., Cipolla, R.: Fast Visual Tracking by Temproal Consensus; Image and Vision Processing 14, pp. 105–114, 1996.
Gennery, D.B.: Visual Tracking of Known Three-Dimensional Objects; Int. J. of Computer Vision Vol. 7(3), pp. 243–270, 1992.
Hashimoto, K.: Visual Servoing; World Scientific, 1993.
G. Hager, K. Toyama, The XVision-System: A Portable Substrate for Real-Time Vision Applications, Computer Vision and Image Understanding 69(1), pp. 23–37, 1998.
Heuring, J.J., Murray, D.W.: Visual Head Tracking and Slaving for Visual Tele-presence; ICRA, pp. 2908–2914, 1996.
Kosaka, A., Nakazawa, G.: Vision-Based Motion Tracking of Rigid Objects Using Prediction of Uncertainties; ICRA, pp. 2637–2644, 1995.
Krotkov, E., Bajcsy, R.: Active Vision for Reliable Ranging: Cooperating Focus, Stereo, and Vergence; Int. J. of Computer Vision Vol. 11(2), pp. 187–203, 1993.
Krautgartner, P., Vincze, M.: Performance Evaluation of Vision-Based Control Tasks; accepted for publication at IEEE ICRA, Leuven, 1998.
Landy, Maloney, Johnston, Young: Vision Research, 35, 389–412.
Lanser, S., Zierl, C.: On the Use of Topological Constraints within Object Recognition Tasks; 13th ICPR, Vienna 1996.
Lowe, D.G.: Robust Model-Based Motion Tracking Through the Integration of Search and Estimation; Int. J. of Computer Vision Vol. 8(2), pp. 113–122, 1992.
Magarey, J., Kokaram, A., Kingsbury, N.: Robust Motion Estimation Using Chrominance Information in Colour Image Sequences; Lecture Notes in Computer Science, Springer Vol. 1310, pp. 486–493, 1997.
Schaber, L.: Echtzeit-Sensorschnittstelle zur Ansteuerung eines Roboters unter Linux; Diploma Thesis, Inst. of Flexible Automation, Vienna University of Technology, 1997.
Y. Shirai, Y. Mae, S. Yamamoto: Object Tracking by Using Optical Flows and Edges; 7th Int. Symp. on Robotics Research, pp. 440–447, 1995.
T. F. Syeda-Mahmood: Data and Model-Driven Selection Using Color Regions; Int. Journal of Computer Vision, Vol. 21(1/2), pp. 9–36, 1997.
Tonko, M., Sch”fer, K., Heimes, F., Nagel, H.H.: Towards Visually Servoed Manipulation of Car Engine Parts; ICRA, pp. 3166–3171, 1997.
K. Toyama, G.D. Hager: If at First You Don’t Succeed..., Study of Pre-and Post Failure Handling; Int. Conf. AAAI, Providence, pp. A3–9, August 1997.
Vincze, M., Ayromlou, M.: Robust Vision for Pose Control of a Robot; Proc. 22nd Workshop of the Austrian Association for Pattern Recongition ÖAGM’ 98, pp. 135–144, 1998.
Wilson, W.J., Williams Hulls, C.C., Bell, G.S.: Relative End-Effector Control Using Cartesian Position Based Visual Servoing; IEEE Trans. on RA Vol. 12(5), pp. 684–696, 1996.
Wren, C.R., Azarbayejani, A., Darrell, T., Pentland, A.P.: Pfinder: Real-Time Tracking of the Human Body; IEEE Transactions on Pattern Analysis and Machine IntelligenceVol. 19(7), pp. 780–785, 1997.
Wunsch, P., Hirzinger, G.: Real-Time Visual Tracking of 3D-Objects with Dynamic Handling of Occlusion; IEEE ICRA, 1997.
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Vincze, M., Ayromlou, M., Kubinger, W. (1999). An Integrating Framework for Robust Real-Time 3D Object Tracking. In: Computer Vision Systems. ICVS 1999. Lecture Notes in Computer Science, vol 1542. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-49256-9_9
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DOI: https://doi.org/10.1007/3-540-49256-9_9
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