Temporal sequence recognition using uncertain sensor data

Author

Rombaut, M. ; Loriette-Rougegrez, S. ; Nigro, J.M. ; Jarkass, I.

Author_Institution

CREATIS, Lyon, France

Volume

2

fYear

2000

fDate

10-13 July 2000

Abstract

The problem addressed in the paper concerns temporal sequence recognition for a dynamic system. Several formal models can be used such as rule based systems, or graphs such as transition graphs or Petri nets in order to describe the sequences to be recognized. Then, according to the inputs obtained from the system´s sensors at different times, the goal is to evaluate confidence into the fact that the sequence is in progress. The confidence is modeled by a distribution of mass of evidence proposed in Dempster-Shafer´s theory.

Keywords

Petri nets; belief networks; inference mechanisms; knowledge based systems; pattern recognition; sensor fusion; sequences; temporal logic; uncertainty handling; Dempster-Shafer theory; Petri nets; dynamic system; formal models; mass of evidence distribution; rule based systems; system sensors; temporal sequence recognition; transition graphs; uncertain sensor data; Artificial intelligence; Intelligent sensors; Knowledge based systems; Petri nets; Psychology; Sensor systems; Uncertainty; Vehicle driving; Vehicle dynamics; Vehicles;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Fusion, 2000. FUSION 2000. Proceedings of the Third International Conference on

Conference_Location

Paris, France

Print_ISBN

2-7257-0000-0

Type

conf

DOI

10.1109/IFIC.2000.859834

Filename

859834