DocumentCode :
2880675
Title :
A pattern recognition and feature fusion formulation for vehicle reidentification in Intelligent Transportation Systems
Author :
Ramachandran, Ravi P. ; Arr, Glenn ; Sun, Carlos ; Ritchie, Stephen G.
Author_Institution :
Rowan University, Glassboro, NJ 08028, USA
Volume :
4
fYear :
2002
fDate :
13-17 May 2002
Abstract :
Vehicle reidentification is the process of reidentifying or tracking vehicles from one point on the roadway to the next. By performing vehicle reidentification, important traffic parameters including travel time, section density and partial dynamic origin/destination demands can be obtained. This provides for anonymous tracking of vehicles from site-to-site and has the potential for improving Intelligent Transportation Systems (ITS) by providing more accurate data. This paper presents a new vehicle reidentification algorithm that uses four different features, namely, (I) the inductive signature vector acquired from loop detectors, (2) vehicle velocity, (3) traversal time and (4) color information (based on images acquired from video cameras) to achieve high accuracy. A nearest neighbor approach classifies the features and linear feature fusion is shown to improve performance. With the fusion of four features, more than a 91 percent accuracy is obtained on real data collected from a parkway in California.
Keywords :
Pattern recognition; Variable speed drives; Vehicles;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech, and Signal Processing (ICASSP), 2002 IEEE International Conference on
Conference_Location :
Orlando, FL, USA
ISSN :
1520-6149
Print_ISBN :
0-7803-7402-9
Type :
conf
DOI :
10.1109/ICASSP.2002.5745494
Filename :
5745494
Link To Document :
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