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Online Video Stabilization Based on Particle Filters

11 years 7 months ago
Online Video Stabilization Based on Particle Filters
Particle filters have been introduced as a powerful tool to estimate the posterior density of nonlinear systems. These filters are also capable of processing data online as required in many practical applications. In this paper, we propose a novel technique for video stabilization based on the particle filtering framework. Scale-invariant feature points are extracted to form a rough estimate which is used to model the importance density. We use a constant-velocity Kalman filter model to estimate intentional camera movement. We also prove that the particle filtering estimate will lower the error variance. The superior performance and robustness of our algorithm is demonstrated by computer simulations.
Chong Chen, Dan Schonfeld, Junlan Yang, Magdi A. M
Added 22 Oct 2009
Updated 27 Oct 2009
Type Conference
Year 2006
Where ICIP
Authors Chong Chen, Dan Schonfeld, Junlan Yang, Magdi A. Mohamed
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