NDLI: Monte Carlo Tracking on the Riemannian Manifold of Multivariate Normal Distributions

Content Provider	IEEE Xplore Digital Library
Author	Snoussi, H. Richard, C.
Copyright Year	2009
Description	Author affiliation: Charles Delaunay Institute, FRE CNRS 2848, University of Technology of Troyes, France (Richard, C.) \|\| Charles Delaunay Institute, FRE CNRS 2848, University of Technology of Troyes, France, Email = Hichem.Snoussi@utt.fr (Snoussi, H.)
Abstract	In this contribution, a general scheme of particle filtering on Riemannian manifolds is proposed. In addition to the nonlinear dynamics, the system state is constrained to lie on a Riemannian manifold M, which dimension is much lower than the whole embedding space dimension. The Riemannian manifold. formulation of the state space model avoids the curse of dimensionalilty from which suffers most of the particle filter methods. Furthermore, this formulation is the only natural tool when the embedding Euclidean space cannot be defined (the state space is defined in an abstract geometric way) or when the constraints are not easily handled (space of positive definite matrices).
Starting Page	280
Ending Page	285
File Size	5023444
Page Count	6
File Format	PDF
ISBN	9781424436774
DOI	10.1109/DSP.2009.4785935
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-01-04
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Monte Carlo methods Gaussian distribution Filtering Target tracking Bayesian methods State estimation Particle filters Covariance matrix Yttrium Stochastic resonance
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Probabilistic tracking on Riemannian manifolds

The Gaussian Particle multi-target multi-Bernoulli filter

Non-linear state estimation by Monte Carlo filters: a six-dimensional example

Evolutionary Optimization of Dynamics Models in Sequential Monte Carlo Target Tracking

Applied particle filter in traffic tracking

Comments on Gaussian particle filtering

Tracking video target via particle filtering on the lie group normal distribution

Gaussian particle filtering

Hybrid Monte Carlo filtering: edge-based people tracking

Monte Carlo Tracking on the Riemannian Manifold of Multivariate Normal Distributions

Similar Documents

Probabilistic tracking on Riemannian manifolds

The Gaussian Particle multi-target multi-Bernoulli filter

Non-linear state estimation by Monte Carlo filters: a six-dimensional example

Evolutionary Optimization of Dynamics Models in Sequential Monte Carlo Target Tracking

Applied particle filter in traffic tracking

Comments on Gaussian particle filtering

Tracking video target via particle filtering on the lie group normal distribution

Gaussian particle filtering

Hybrid Monte Carlo filtering: edge-based people tracking

Monte Carlo Tracking on the Riemannian Manifold of Multivariate Normal Distributions