NDLI: Recursive Camera-Motion Estimation With the Trifocal Tensor

Content Provider	IEEE Xplore Digital Library
Author	Ying Kin Yu Kin Hong Wong Chang, M.M.Y. Siu Hang Or
Copyright Year	1996
Abstract	In this paper, an innovative extended Kalman filter (EKF) algorithm for pose tracking using the trifocal tensor is proposed. In the EKF, a constant-velocity motion model is used as the dynamic system, and the trifocal-tensor constraint is incorporated into the measurement model. The proposed method has the advantages of those structure- and-motion-based approaches in that the pose sequence can be computed with no prior information on the scene structure. It also has the strengths of those model-based algorithms in which no updating of the three-dimensional (3-D) structure is necessary in the computation. This results in a stable, accurate, and efficient algorithm. Experimental results show that the proposed approach outperformed other existing EKFs that tackle the same problem. An extension to the pose-tracking algorithm has been made to demonstrate the application of the trifocal constraint to fast recursive 3-D structure recovery
Page Count	10
File Size	838297
Starting Page	1081
Ending Page	1090
File Format	PDF
ISSN	10834419
Volume Number	36
Issue Number	5
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-10-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Recursive estimation Tensile stress Layout Cameras Tracking Augmented reality Image sequences Iterative algorithms Design automation Motion measurement trifocal tensor Kalman filtering pose tracking structure and motion (SAM)
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Information Systems Electrical and Electronic Engineering Human-Computer Interaction Computer Science Applications Software

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