Abstract
In this paper we apply a novel pose estimation algorithm to the tracking problem. We make use of error measures of the algorithm which enable us to characterize the quality of an estimated pose. The key idea of the tracking algorithm is random start local search. The principle of the heuristic relies upon a combination of iterative improvement and random sampling. While in many approaches a manually designed object representation is assumed, we overcome this condition by using accumulated object representations and combine these successfully with the tracking algorithm.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
M. Ackermann: Akkumulieren von Objektrepräsentationen im Wahrnehmungs-Handlungs Zyklus. Diplomarbeit,Le hrstuhl für Kognitive Systeme der Christian-Albrechts-Universität zu Kiel,( 2000). 11
E. Bayro-Corrochano: The geometry and algebra of kinematics. In: Geometric Computing with Clifford Algebra (Sommer G.,ed.), Springer, Berlin (2000). 12
P. V. C. Hough: Methods and means for recognizing complex patterns. U. S. Patent 3,069,654, (Dec. 18, 1962). 10
Norbert Krüger, Marcus Ackermann, Gerald Sommer: Accumulation of object representations utilizing interaction of robot action and perception. DAGM 2000 (2000) 365–372. 10, 15
Norbert Krüger, Daniel Wendor., Gerald Sommer: Two models of a vision-based robotic system: visual haptic attention and accumulation of object representations. In:Proceed. Robot Vision 2001,Auckland (2001). 15, 17
S. Kunze: Ein Hand-Auge-System zur visuell basierten Lokalisierung und Identifikation von Objekten. Diplomarbeit,Le hrstuhl für Kognitive Systeme der Christian-Albrechts-Universität zu Kiel, (2000). 11
J. Pauli: Geometric/photometric consensus and regular shape quasi-invariants for object localization and boundary extraction. Technical Report 9805, Christian-Albrechts-Universität zu Kiel,Ins titut für Informatik und Praktische Mathematik, (1998). 11
J. Princen, J. Illingworth, J. Kittler: An optimizing line finder using a Hough transform algorithm. Computer Vision, Graphics, and Image Processing 52 (1990) 57–77. 10
B. Rosenhahn, Y. Zhang, G. Sommer: Pose estimation in the language of kinematics. 2nd Internat. Workshop Algebraic Frames for the Perception-Action Cycle, AFPAC 2000, LNCS 1888 (2000) 284–293. 11
J. R. Beveridge: Local search algorithms for geometric object recognition: Optimal correspondence and pose. Technical Report CS 93-5, University of Massachusetts, (1993). 10, 13
G. Sommer, B. Rosenhahn, Y. Zang: Pose estimation using geometric constraints. Technical Report 2003, Christian-Albrechts-Universität zu Kiel,Ins titut für Informatik und Praktische Mathematik,( 2000). 9, 10, 11, 13, 14, 16, 17
Y. Zhang, B. Rosenhahn, G. Sommer: Extended Kalman filter design for motion estimation by point and line observations. 2nd Internat.Workshop Algebraic Frames for the Perception-Action Cycle,A FPAC 2000, LNCS 1888 (2000) 339–348. 13
Y. Zhang, G. Sommer: 3D rigid object tracking in computer vision. Seminar presentation, Oberseminar, Institut für Informatik und Praktische Mathematik, Christian-Albrechts-Universität zu Kiel (July 2000). 14
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Rosenhahn, B., Krüger, N., Rabsch, T., Sommer, G. (2001). Tracking with a Novel Pose Estimation Algorithm. In: Klette, R., Peleg, S., Sommer, G. (eds) Robot Vision. RobVis 2001. Lecture Notes in Computer Science, vol 1998. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44690-7_2
Download citation
DOI: https://doi.org/10.1007/3-540-44690-7_2
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-41694-4
Online ISBN: 978-3-540-44690-3
eBook Packages: Springer Book Archive