High Accuracy Optical Flow Serves 3-D Pose Tracking: Exploiting Contour and Flow Based Constraints
Tracking the 3-D pose of an object needs correspondences between 2-D features in the image and their 3-D counterparts in the object model. A large variety of such features has been suggested in the literature. All of them have drawbacks in one situation or the other since their extraction in the image and/or the matching is prone to errors. In this paper, we propose to use two complementary types of features for pose tracking, such that one type makes up for the shortcomings of the other. Aside from the object contour, which is matched to a free-form object surface, we suggest to employ the optic flow in order to compute additional point correspondences. Optic flow estimation is a mature research field with sophisticated algorithms available. Using here a high quality method ensures a reliable matching. In our experiments we demonstrate the performance of our method and in particular the improvements due to the optic flow.
KeywordsBlock Match Point Correspondence Contour Extraction Constancy Assumption Pose Estimation
Unable to display preview. Download preview PDF.
- 8.David, P., DeMenthon, D., Duraiswami, R., Samet, H.: Simultaneous pose and correspondence determination using line features. In: Proc. 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 424–431 (2003)Google Scholar
- 11.Goddard, J.: Pose and motion estimation from vision using dual quaternion-based extended Kalman filtering. Technical report, University of Tennessee, Knoxville (1997)Google Scholar
- 12.Grimson, W.E.L.: Object Recognition by Computer. MIT Press, Cambridge (1990)Google Scholar
- 15.Lowe, D.: Solving for the parameters of object models from image descriptions. In: Proc. ARPA Image Understanding Workshop, pp. 121–127 (1980)Google Scholar
- 22.Rosenhahn, B.: Pose estimation revisited. Technical Report TR-0308, Institute of Computer Science, University of Kiel, Germany (October 2003)Google Scholar
- 25.Shevlin, F.: Analysis of orientation problems using Plücker lines. In: International Conference on Pattern Recognition (ICPR), Brisbane, vol. 1, pp. 685–689 (1998)Google Scholar
- 26.Vacchetti, L., Lepetit, V., Fua, P.: Combining edge and texture information for real-time accurate 3D camera tracking. In: 3rd International Symposium on Mixed and Augmented Reality, pp. 48–57 (2004)Google Scholar