Data fusion methods based on fuzzy measures in vehicle classification process

Author

Sroka, Ryszard

Author_Institution

Dept. of Meas. & Instrum., AGH-Univ. of Sci. & Technol., Krakow, Poland

Volume

3

fYear

2004

fDate

18-20 May 2004

Firstpage

2234

Abstract

The paper presents results of analysis and properties comparison of five different data fusion methods in process of vehicle classification. The fusion process has been realized on basis of signals features. The used signals comes from inductive loop and piezoelectric sensors placed in surface of the road. The models of vehicle classes have been defined by using fuzzy measures with triangular and gaussian shapes. The paper presents the construction method of such models and advantages of data fusion methods.

Keywords

fuzzy logic; pattern classification; road traffic; sensor fusion; traffic engineering computing; data fusion methods; fusion algorithms; fuzzy logic; fuzzy measures; gaussian shapes; inductive loop sensors; object classification; piezoelectric sensors; road surface; signal features; traffic jams; triangular shapes; vehicle classification process; Artificial intelligence; Axles; Instruments; Magnetic sensors; Motion measurement; Sensor systems; Shape; Signal processing; Vehicles; Velocity measurement;

fLanguage

English

Publisher

ieee

Conference_Titel

Instrumentation and Measurement Technology Conference, 2004. IMTC 04. Proceedings of the 21st IEEE

ISSN

1091-5281

Print_ISBN

0-7803-8248-X

Type

conf

DOI

10.1109/IMTC.2004.1351536

Filename

1351536