AMDO 2004: Articulated Motion and Deformable Objects pp 189-201 | Cite as
Complex Articulated Object Tracking
Abstract
In this paper new results are presented for tracking complex multi-body objects. The theoretical framework is based on robotics techniques and uses an a-priori model of the object including a general mechanical link description. A new kinematic-set formulation takes into account that articulated degrees of freedom are directly observable from the camera and therefore their estimation does not need to pass via a kinematic-chain back to the root. By doing this the tracking techniques are efficient and precise leading to real-time performance and accurate measurements. The system is locally based upon an accurate modeling of a distance criteria. A general method is given for defining any type of mechanical link and experimental results show prismatic, rotational and helical type links. A statistical M-estimation technique is applied to improve robustness. A monocular camera system was used as a real-time sensor to verify the theory.
Preview
Unable to display preview. Download preview PDF.
References
- 1.Aggarwal, J., Cai, Q., Liao, W., Sabata, B.: Nonrigid motion analysis: Articulated and elastic motion. Computrer Vision and Image Understanding 70, 142–156 (1998)CrossRefGoogle Scholar
- 2.Lowe, D.: Three-dimensional object recognition from single two-dimensional images. Artificial Intelligence 31, 355–394 (1987)CrossRefGoogle Scholar
- 3.Drummond, T., Cipolla, R.: Real-time visual tracking of complex structures. IEEE Trans. on Pattern Analysis and Machine Intelligence 27, 932–946 (2002)CrossRefGoogle Scholar
- 4.Marchand, E., Bouthemy, P., Chaumette, F., Moreau, V.: Robust real-time visual tracking using a 2d-3d model-based approach. In: IEEE Int. Conf. on Computer Vision, ICCV 1999, Kerkira, Greece, vol. 1, pp. 262–268 (1999)Google Scholar
- 5.Comport, A.I., Marchand, E., Chaumette, F.: A real-time tracker for markerless augmented reality. In: ACM/IEEE Int. Symp. on Mixed and Augmented Reality, ISMAR 2003, Tokyo, Japan, pp. 36–45 (2003)Google Scholar
- 6.Dhome, M., Richetin, M., Lapresté, J.T., Rives, G.: Determination of the attitude of 3-d objects from a single perspective view. IEEE Trans. on Pattern Analysis and Machine Intelligence 11, 1265–1278 (1989)CrossRefGoogle Scholar
- 7.Dementhon, D., Davis, L.: Model-based object pose in 25 lines of codes. Int. J. of Computer Vision 15, 123–141 (1995)CrossRefGoogle Scholar
- 8.Lu, C., Hager, G., Mjolsness, E.: Fast and globally convergent pose estimation from video images. IEEE Trans. on Pattern Analysis and Machine Intelligence 22, 610–622 (2000)CrossRefGoogle Scholar
- 9.Lowe, D.: Fitting parameterized three-dimensional models to images. IEEE Trans. on Pattern Analysis and Machine Intelligence 13, 441–450 (1991)CrossRefGoogle Scholar
- 10.Nunomaki, T., Yonemoto, S., Arita, D., Taniguchi, R.: Multipart non-rigid object tracking based on time model-space gradients. In: Articulated Motion and Deformable Objects First International Workshop, pp. 78–82 (2000)Google Scholar
- 11.Ruf, A., Horaud, R.: Rigid and articulated motion seen with an uncalibrated stereo rig. In: IEEE Int. Conf. on Computer Vision, Corfu, Greece, pp. 789–796 (1999)Google Scholar
- 12.Marchand, E., Chaumette, F.: Virtual visual servoing: a framework for realtime augmented reality. In: EUROGRAPHICS 2002 Conference Proceeding, Saarebrücken, Germany. Computer Graphics Forum, vol. 21(3), pp. 289–298 (2002)Google Scholar
- 13.Huber, P.J.: Robust Statistics. Wiler, New York (1981)MATHCrossRefGoogle Scholar
- 14.Hutchinson, S., Hager, G., Corke, P.: A tutorial on visual servo control. IEEE Trans. on Robotics and Automation 12, 651–670 (1996)CrossRefGoogle Scholar
- 15.Espiau, B., Chaumette, F., Rives, P.: A new approach to visual servoing in robotics. IEEE Trans. on Robotics and Automation 8, 313–326 (1992)CrossRefGoogle Scholar
- 16.Comport, A.I., Marchand, E., Chaumette, F.: Object-based visual 3d tracking of articulated objects via kinematic sets. In: IEEE Workshop on Articulated and Non-Rigid Motion, Washington, DC (2004)Google Scholar
- 17.Fischler, N., Bolles, R.: Random sample consensus: A paradigm for model fitting with application to image analysis and automated cartography. Communication of the ACM 24, 381–395 (1981)CrossRefMathSciNetGoogle Scholar