Title
Partial Linear Gaussian Models for Tracking in Image Sequences Using Sequential Monte Carlo Methods
Abstract
The recent development of Sequential Monte Carlo methods (also called particle filters) has enabled the definition of efficient algorithms for tracking applications in image sequences. The efficiency of these approaches depends on the quality of the state-space exploration, which may be inefficient due to a crude choice of the function used to sample in the associated probability space. A careful study of this issue led us to consider the modeling of the tracked dynamic system with partial linear Gaussian models. Such models are characterized by a non linear dynamic equation, a linear measurement equation and additive Gaussian noises. They allow inferring an analytic expression of the optimal importance function used in the diffusion process of the particle filter, and enable building a relevant approximation of a validation gate. Despite of these potential advantages partial linear Gaussian models have not been investigated. The aim of this paper is therefore to demonstrate that such models can be of real interest facing difficult usual issues such as occlusions, ambiguities due to cluttered backgrounds and large state space. Three instances of these models are proposed. After a theoretical analysis, their significance is demonstrated by their performance for tracking points and planar objects in challenging real-world image sequences.
Year
DOI
Venue
2007
10.1007/s11263-006-0003-2
International Journal of Computer Vision
Keywords
DocType
Volume
non linear dynamic equation,optimal importance function,associated probability space,image sequence,real-world image sequence,planar structure tracking,validation gate,sequential monte carlo methods,tracked dynamic system,rao-blackwellization,point tracking,additive gaussian noise,large state space,partial linear gaussian models,partial linear gaussian model,particle filter,linear measurement equation,non linear dynamics,dynamic system,diffusion process,gaussian noise,state space
Journal
74
Issue
ISSN
Citations 
1
1573-1405
14
PageRank 
References 
Authors
0.67
32
2
Name
Order
Citations
PageRank
Elise Arnaud112610.05
Etienne Mémin218313.94