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Article

Singularity Analysis for Articulated Object Tracking

Authors:

J. M. RehgAuthors Info & Claims

CVPR '98: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Page 289

Published: 23 June 1998 Publication History

Abstract

We analyze the use of kinematic constraints for articulated object tracking. Conditions for the occurrence of singularities in 3-D models are presented and their effects on tracking are characterized. We describe a novel 2-D Scaled Prismatic Model (SPM) for figure registration. In contrast to 3-D kinematic models. the SPM has fewer singularity problems and does not require detailed knowledge of the 3-D kinematics. We fully characterize the singularities in the SPM and illustrate tracking through singularities using synthetic and real examples with 3-D and 2-D models. Our results demonstrate the significant benefits of the SPM in tracking with a single source of video.

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Singularity Analysis for Articulated Object Tracking
1. Computing methodologies
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cover image Guide Proceedings

CVPR '98: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

June 1998

ISBN:0818684976

Copyright © Copyright © 1998 IEEE. All Rights Reserved.

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IEEE Computer Society

United States

Publication History

Published: 23 June 1998

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Lee TBelkhatir MSanei S(2014)A comprehensive review of past and present vision-based techniques for gait recognitionMultimedia Tools and Applications10.1007/s11042-013-1574-x72:3(2833-2869)Online publication date: 1-Oct-2014
https://dl.acm.org/doi/10.1007/s11042-013-1574-x
Lin HChen TChen CHsieh CLie W(2009)Human pose estimation from monocular image capturesProceedings of the 2009 IEEE international conference on Multimedia and Expo10.5555/1698924.1699168(994-997)Online publication date: 28-Jun-2009
https://dl.acm.org/doi/10.5555/1698924.1699168
Zhu YFujimura K(2009)Bayesian 3d human body pose tracking from depth image sequencesProceedings of the 9th Asian conference on Computer Vision - Volume Part II10.1007/978-3-642-12304-7_26(267-278)Online publication date: 23-Sep-2009
https://dl.acm.org/doi/10.1007/978-3-642-12304-7_26
Siddiqui MMedioni G(2007)Efficient upper body pose estimation from a single image or a sequenceProceedings of the 2nd conference on Human motion: understanding, modeling, capture and animation10.5555/1785357.1785364(74-87)Online publication date: 20-Oct-2007
https://dl.acm.org/doi/10.5555/1785357.1785364
Izo TGrimson W(2007)Simultaneous pose recovery and camera registration from multiple views of a walking personImage and Vision Computing10.1016/j.imavis.2005.10.00325:3(342-351)Online publication date: 1-Mar-2007
https://dl.acm.org/doi/10.1016/j.imavis.2005.10.003
Medioni GFrançois ASiddiqui MKim KYoon H(2007)Robust real-time vision for a personal service robotComputer Vision and Image Understanding10.1016/j.cviu.2006.10.015108:1-2(196-203)Online publication date: 1-Oct-2007
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Siddiqui MMedioni GAggarwal JCucchiara RPrati A(2006)Robust real-time upper body limb detection and trackingProceedings of the 4th ACM international workshop on Video surveillance and sensor networks10.1145/1178782.1178792(53-60)Online publication date: 27-Oct-2006
https://dl.acm.org/doi/10.1145/1178782.1178792
Mori GMalik J(2006)Recovering 3D Human Body Configurations Using Shape ContextsIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2006.14928:7(1052-1062)Online publication date: 1-Jul-2006
https://dl.acm.org/doi/10.1109/TPAMI.2006.149
Sminchisescu CTriggs B(2005)Building Roadmaps of Minima and Transitions in Visual ModelsInternational Journal of Computer Vision10.1023/B:VISI.0000042935.43630.4661:1(81-101)Online publication date: 1-Jan-2005
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Fei HReid I(2003)Probabilistic Tracking and Recognition of Non-Rigid Hand MotionProceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures10.5555/946242.946285Online publication date: 17-Oct-2003
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