Confluence of parameters in model based tracking

During the last decade, model based tracking of objects and its necessity in visual servoing and manipulation has been advocated in a number of systems. Most of these systems demonstrate robust performance for cases where either the background or the object are relatively uniform in color. In terms of manipulation, our basic interest is handling of everyday objects in domestic environments such as a home or an office. In this paper, we consider a number of different parameters that effect the performance of a model-based tracking system. Parameters such as color channels, feature detection, validation gates, outliers rejection and feature selection are considered here and their affect to the overall system performance is discussed. Experimental evaluation shows how some of these parameters can successfully be evaluated (learned) on-line and consequently improve the performance of the system.

[1]  Chris Harris,et al.  RAPID - a video rate object tracker , 1990, BMVC.

[2]  Thomas Ertl,et al.  Computer Graphics - Principles and Practice, 3rd Edition , 2014 .

[3]  Rachid Deriche,et al.  From Noisy Edge Points to 3D Reconstruction of a Scene: A Robust Approach and its Uncertainty Analysis , 1992 .

[4]  Larry S. Davis,et al.  New exact and approximate solutions of the three-point perspective problem , 1990, Proceedings., IEEE International Conference on Robotics and Automation.

[5]  David G. Lowe,et al.  Robust model-based motion tracking through the integration of search and estimation , 1992, International Journal of Computer Vision.

[6]  M. Carter.  Computer graphics: Principles and practice , 1997 .

[7]  Hans-Hellmut Nagel,et al.  Model-based object tracking in monocular image sequences of road traffic scenes , 1993, International Journal of Computer 11263on.

[8]  Larry S. Davis,et al.  Exact and Approximate Solutions of the Perspective-Three-Point Problem , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Andrew Zisserman,et al.  Robust Object Tracking , 2001 .

[10]  Helder Araujo,et al.  A Fully Projective Formulation for Lowe's Tracking Algorithm , 1996 .

[11]  Danica Kragic,et al.  Model based techniques for robotic servoing and grasping , 2002, IEEE/RSJ International Conference on Intelligent Robots and Systems.

[12]  Peter I. Corke,et al.  A tutorial on visual servo control , 1996, IEEE Trans. Robotics Autom..

[13]  Roy Eagleson,et al.  Human visual servoing for reaching and grasping: the role of 3D geometric features , 1999, Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C).

[14]  Hiroshi Murase,et al.  Subspace methods for robot vision , 1996, IEEE Trans. Robotics Autom..

[15]  Roberto Cipolla,et al.  Real-Time Tracking of Multiple Articulated Structures in Multiple Views , 2000, ECCV.

[16]  Markus Vincze,et al.  An Integrated Framework for Robust Real-Time 3D Object Tracking , 1999, ICVS.

[17]  Danica Kragic.  Visual Servoing for Manipulation : Robustness and Integration Issues , 2001 .

[18]  Gerd Hirzinger,et al.  Real-time visual tracking of 3D objects with dynamic handling of occlusion , 1997, Proceedings of International Conference on Robotics and Automation.

[19]  大野 義夫,et al.  Computer Graphics : Principles and Practice, 2nd edition, J.D. Foley, A.van Dam, S.K. Feiner, J.F. Hughes, Addison-Wesley, 1990 , 1991 .

[20]  Danny Roobaert.  Pedagogical support vector learning : a pure learning approach to object recognition , 2001 .

[21]  Michael Isard,et al.  CONDENSATION—Conditional Density Propagation for Visual Tracking , 1998, International Journal of Computer Vision.

[22]  Jan-Olof Eklundh,et al.  A pure learning approach to background-invariant object recognition using pedagogical support vector learning , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.