Abstract
Model-based 3-D object tracking has earned significant importance in areas such as augmented reality, surveillance, visual servoing, robotic object manipulation and grasping. Key problems to robust and precise object tracking are the outliers caused by occlusion, self-occlusion, cluttered background, reflections and complex appearance properties of the object. Two of the most common solutions to the above problems have been the use of robust estimators and the integration of visual cues. The tracking system presented in this paper achieves robustness by integrating model-based and model-free cues together with robust estimators. As a model-based cue, a wireframe edge model is used. As model-free cues, automatically generated surface texture features are used. The particular contribution of this work is the integration framework where not only polyhedral objects are considered. In particular, we deal also with spherical, cylindrical and conical objects for which the complete pose cannot be estimated using only wireframe models. Using the integration with the model-free features, we show how a full pose estimate can be obtained. Experimental evaluation demonstrates robust system performance in realistic settings with highly textured objects and natural backgrounds.
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Vacchetti L., Lepetit V., Fua P.: Stable real-time 3D tracking using online and offline information. IEEE Trans. Pattern Anal. Mach. Intell. 26, 1385–1391 (2004)
Vincze M., Ayromlou M., Ponweiser M., Zillich M.: Edge projected integration of image and model cues for robust model-based object tracking. Int. J. Robot. Res. 20(7), 533–552 (2001)
Taylor, G., Kleeman, L.: Fusion of multimodal visual cues for model-based object tracking. In: Australiasian Conf. on Robotics and Automation, Brisbane, Australia (2003)
Harris C.: Tracking with rigid models. In: Blake, A., Yuille, A. (eds) Active Vision, Ch. 4, pp. 59–73. MIT Press, Cambridge (1992)
Comport, A., Marchand, E., Chaumette, F.: A real-time tracker for markerless augmented reality. In: IEEE Int. Symp. on Mixed and Augmented Reality pp. 36–45 (2003)
Vincze, M., Ayromlou, M., Kubinger, W.: An integrating framework for robust real-time 3D object tracking. In: Int. Conf. on Comp. Vis. Syst., ICVS’99, pp. 135–150 (1999)
Koller D., Daniilidis K., Nagel H.: Model-based object tracking in monocular image sequences of road traffic scenes. Int. J. Comput. Vis. 10(3), 257–281 (1993)
Drummond T., Cipolla R.: Real-time visual tracking of complex structures. IEEE Trans. PAMI 24(7), 932–946 (2002)
Malis E., Chaumette F., Boudet S.: 2-1/2-d visual servoing. IEEE Trans. Robot. Autom. 15(2), 238–250 (1999)
Jurie F., Dhome M.: Real time tracking of 3D objects: an efficient and robust approach. Pattern Recognit. 35, 317–328 (2002)
Dickmanns E.D., Graefe V.: Dynamic monocular machine vision. Mach. Vis. Appl. 1, 223–240 (1988)
Lowe D.G.: Robust model-based motion tracking through the integration of search and estimation. Int. J. Comput. Vis. 8(2), 113–122 (1992)
Wunsch, P., Hirzinger, G.: Real-time visual tracking of 3-D objects with dynamic handling of occlusion. In: IEEE Int. Conf. on Robotics and Automation, ICRA’97, Albuquerque, New Mexico, USA, pp. 2868–2873 (1997)
Masson, L., Jurie, F., Dhome, M.: Robust real time tracking of 3D objects. In: Int. Conf. Pattern Recognit., vol. 4, pp. 252–255 (2004)
Klein, G., Drummond, T.: Robust visual tracking for non-instrumented augmented reality. In: Proc. 2nd IEEE and ACM International Symposium on Mixed and Augmented Reality, pp. 113–122 (2003)
Rasmussen C., Hager G.: Probabilistic data association methods for tracking complex visual objects. IEEE Trans. PAMI 23(6), 560–576 (2001)
Triesch, J., der Malsburg, C.V.: Self-organized integration of adaptive visual cues for face tracking. In: Fourth IEEE International Conference on Automatic Face and Gesture Recognition, pp. 102–107 (2000)
Kragic D., Christensen H.I.: Cue integration for visual servoing. IEEE Trans. Robot. Autom. 17(1), 18–27 (2001)
Hayman, E., Eklundh, J.-O.: Statistical background subtraction for a mobile observer. In: Proceedings of the Ninth IEEE International Conference on Computer Vision, pp. 67–74 (2003)
Vacchetti, L., Lepetit, V., Fua, P.: Combining edge and texture information for real-time accurate 3D camera tracking. In: Proceedings of International Symposium on Mixed and Augmented Reality, Arlington, VA, USA (2004)
Rosten, E., Drummond, T.: Fusing points and lines for high performance tracking. In: IEEE International Conference on Computer Vision, vol. 2, pp. 1508–1511. Beijing, China (2005)
Kyrki, V., Kragic, D.: Integration of model-based and model-free cues for visual object tracking in 3D. In: IEEE International Conference on Robotics and Automation, ICRA’05, pp. 1566–1572 (2005)
Kyrki, V., Schmock, K.: Integation methods of model-free features for 3D tracking. In: Scandinavian Conference on Image Analysis, pp. 557–566 (2005)
Brox, T., Rosenhahn, B., Cremers, D., Seidel, H.P.: High accuracy optical flow serves 3-D pose tracking: exploiting contour and flow based constraints. In: European Conference on Computer Vision, ECCV 2006, Graz, Austria, pp. 98–111 (2006)
Pressigout M., Marchand E.: Real-time hybrid tracking using edge and texture information. Int. J. Robot. Res. 26(7), 689–713 (2007)
Hager G., Belhumeur P.: Efficient region tracking with parametric models of geometry and illumination. IEEE Trans. Pattern Anal. Mach. Intell. 20(10), 1025–1039 (1998)
Lu, S., Metaxa, D., Samaras, D., Oliensis, J.: Using multiple cues for hand tracking and model refinement. In: Computer Vision and Pattern Recognition, vol. 2, pp. 443–450. Madison, Wisconsin, USA (2003)
Harris, C.J., Stephens, M.: A combined corner and edge detector. In: Proc. 4th Alvey Vision Conference, Manchester, UK, pp. 147–151 (1988)
Lefebvre T., Bruyninckx H., De Schutter J.: Kalman filters for non-linear systems: a comparison of performance. Int. J. Control 77(7), 639–653 (2004)
Isard M., Blake A.: CONDENSATION-Conditional density propagation for visual tracking. Int. J. Comput. Vis. 29(1), 5–28 (1998)
Bar-Shalom Y., Li X.R., Kirubarajan T.: Estimation with Applications to Tracking and Navigation. Wiley-Interscience, New York (2001)
Welch, G., Bishop, G.: SCAAT: Incremental tracking with incomplete information. In: Proc. Computer graphics and interactive techniques, pp. 333–344. Los Angeles, CA, USA (1997)
Heikkila, J., Silven, O.: A four-step camera calibration procedure with implicit image correction. In: IEEE Computer Vision and Pattern Recognition, pp. 1106–1112 (1997)
Zhang Z.: A flexible new technique for camera calibration. IEEE Trans. Pattern Anal. Mach. Intell. 22(11), 1330–1334 (2000)
Huber P.J.: Robust estimation of a location parameter. Ann. Math. Stat. 35, 73–101 (1964)
Huber P.J.: Robust Statistics. Wiley, New York (1981)
Press W.H., Teukolsky S.A., Vetterling W.T., Flannery B.P.: Numerical Recipes in C++. Cambridge University Press, Cambridge (2002)
Brent R.P.: Algorithms for Minimization without Derivatives. Prentice-Hall, Englewood Cliffs (1973)
Tsai R., Lenz R.K.: A new technique for fully autonomous and efficient 3D robotics hand/eye calibration. IEEE Trans. Robot. Autom. 5(3), 345–358 (1989)
Kyrki, V., Kragic, D., Christensen, H.I.: New shortest-path approaches to visual servoing. In: IEEE/RSJ Int. Conf. Intell. Robots and Systems, Sendai, Japan (2004)
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Kyrki, V., Kragic, D. Tracking rigid objects using integration of model-based and model-free cues. Machine Vision and Applications 22, 323–335 (2011). https://doi.org/10.1007/s00138-009-0214-y
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DOI: https://doi.org/10.1007/s00138-009-0214-y