Forward-Backward-Viterbi Procedures in the Transferable Belief Model for State Sequence Analysis Using Belief Functions

Authors:
Emmanuel Ramasso;Michéle Rombaut;Denis Pellerin
Affiliations:
GIPSA-lab, Images and Signal Department, Grenoble, France 38031;GIPSA-lab, Images and Signal Department, Grenoble, France 38031;GIPSA-lab, Images and Signal Department, Grenoble, France 38031
Venue:
ECSQARU '07 Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Year:
2007

Citing 11
Cited 2

The transferable belief model

Artificial Intelligence
New Semantics for Quantitative Possibility Theory

ECSQARU '01 Proceedings of the 6th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Dynamic bayesian networks: representation, inference and learning

Dynamic bayesian networks: representation, inference and learning
Understanding human behavior from motion imagery

Machine Vision and Applications - Special issue: Human modeling, analysis, and synthesis
The TBM global distance measure for the association of uncertain combat ID declarations

Information Fusion
Multisensor triplet Markov chains and theory of evidence

International Journal of Approximate Reasoning
Human action recognition in videos based on the Transferable Belief Model: Application to athletics jumps

Pattern Analysis & Applications
Refined modeling of sensor reliability in the belief function framework using contextual discounting

Information Fusion
Shape-Motion based athlete tracking for multilevel action recognition

AMDO'06 Proceedings of the 4th international conference on Articulated Motion and Deformable Objects
Partially supervised learning by a credal EM approach

ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Target identification based on the transferable belief model interpretation of dempster-shafer model

IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans

A Generalization of the Pignistic Transform for Partial Bet

ECSQARU '09 Proceedings of the 10th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Particle filtering in the Dempster--Shafer theory

International Journal of Approximate Reasoning

Quantified Score

Hi-index	0.00

Visualization

Abstract

The Transferable Belief Model (TBM) relies on belief functions and enables one to represent and combine a variety of knowledge from certain up to ignorance as well as conflict inherent to imperfect data. A lot of applications have used this flexible framework however, in the context of temporal data analysis of belief functions, a few work have been proposed. Temporal aspect of data is essential for many applications such as surveillance (monitoring) and Human-Computer Interfaces. We propose algorithms based on the mechanisms of Hidden Markov Models usually used for state sequence analysis in probability theory. The proposed algorithms are the "credal forward", "credal backward" and "credal Viterbi" procedures which allow to filter temporal belief functions and to assess state sequences in the TBM framework. Illustration of performance is provided on a human motion analysis problem.