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Pose Estimation from Multiple Cameras Based on Sylvester's Equation

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Title: Pose Estimation from Multiple Cameras Based on Sylvester's Equation
Author(s): Chong, Chen; Dan, Schonfeld
Subject(s): Pose Estimation Distributed Estimation Sylvester's Equation Multiple Cameras
Abstract: In this paper, we introduce a method to estimate the object's pose from multiple cameras. We focus on direct estimation of the 3D object pose from 2D image sequences. Scale-Invariant Feature Transform (SIFT) is used to extract corresponding feature points from adjacent images in the video sequence. We first demonstrate that centralized pose estimation from the collection of corresponding feature points in the 2D images from all cameras can be obtained as a solution to a generalized Sylvester's equation. We subsequently derive a distributed solution to pose estimation from multiple cameras and show that it is equivalent to the solution of the centralized pose estimation based on Sylvester's equation. Specifically, we rely on collaboration among the multiple cameras to provide an iterative re-finement of the independent solution to pose estimation obtained for each camera based on Sylvester's equation. The proposed approach to pose estimation from multiple cameras relies on all of the information available from all cameras to obtain an estimate at each camera even when the image features are not visible to some of the cameras. The resulting pose estimation technique is therefore robust to occlusion and sensor errors from specific camera views. Moreover, the proposed approach does not require matching feature points among images from different camera views nor does it demand reconstruction of 3D points. Furthermore, the computational complexity of the proposed solution grows linearly with the number of cameras. Finally, computer simulation experiments demonstrate the accuracy and speed of our approach to pose estimation from multiple cameras.
Issue Date: 2010-06
Publisher: Elsevier
Citation Info: Chen, C. & Schonfeld, D. 2010. Pose estimation from multiple cameras based on Sylvester's equation. Computer Vision and Image Understanding, 114(6): 652-666. DOI: 10.1016/j.cviu.2010.01.002
Type: Article
Description: Postprint version of article may differ from published version. The definitive version is available through Elsevier at DOI: 10.1016/j.cviu.2010.01.002
URI: http://hdl.handle.net/10027/7208
ISSN: 1077-3142
Date Available in INDIGO: 2011-01-09
 

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