DocumentCode :
1864215
Title :
Robust Homography-Based Trajectory Transformation for Multi-Camera Scene Analysis
Author :
Kayumbi, Gabin ; Cavallaro, Andrea
Author_Institution :
London Univ., London
fYear :
2007
fDate :
25-28 Sept. 2007
Firstpage :
59
Lastpage :
66
Abstract :
In this paper we present a robust image registration algorithm for the estimation of extended object trajectories in a distributed camera setting. The registration algorithm is based on a statistical homography estimation that accounts for errors related to the estimation of the homography matrix. Unlike traditional approaches based on singular value decomposition (SVD), we derive a planar homography estimation from the renormalization technique. We demonstrate the proposed algorithm with the generation of the mosaic of the observed scene as well as with the registration of the spatial locations of moving objects (trajectories) from multiple cameras. Finally, we compare the transformed trajectories with those obtained with the homography estimated by SVD and least mean square (LMS) methods and discuss the improvement in terms of spatial accuracy using objective evaluation metrics on standard test sequences.
Keywords :
image registration; least mean squares methods; singular value decomposition; homography matrix; image registration; least mean square methods; mosaic; multi-camera scene analysis; robust homography; singular value decomposition; statistical homography estimation; trajectory transformation; Cameras; Image analysis; Image registration; Layout; Least squares approximation; Matrix decomposition; Robustness; Singular value decomposition; Testing; Transmission line matrix methods; homography; image registration; mosaic; trajectory transformation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Distributed Smart Cameras, 2007. ICDSC '07. First ACM/IEEE International Conference on
Conference_Location :
Vienna
Print_ISBN :
978-1-4244-1354-6
Electronic_ISBN :
978-1-4244-1354-6
Type :
conf
DOI :
10.1109/ICDSC.2007.4357506
Filename :
4357506
Link To Document :
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