Paper Info
Title
Optimizing parameters of a motion detection system by means of a distributed genetic algorithm
Abstract
The main task of traffic monitoring applications is to identify moving targets. Usually, these applications require that a large number of parameters is tuned in order to work properly. In the motion detection system we have developed, about thirty parameters have been required to be optimized. This paper shows how a distributed implementation of a Genetic Algorithm (GA) over a network of workstations can successfully accomplish the parameter optimization task within a reduced amount of time. Accurate experiments accomplished on a challenging training sequence yield optimal parameter values. Four more test sequences allow us to assess the generality of the results previously attained.
Year
DOI
Venue
2005
10.1016/j.imavis.2005.04.001
Image Vision Comput.
Keywords
Field
DocType
thirty parameter,visual surveillance,reduced amount,genetic algorithm,optimizing parameter,traffic monitoring,motion detection,accurate experiment,optimal parameter value,motion detection system,parameter optimization task,main task,challenging training sequence yield,large number,distributed genetic algorithm,parameter optimization
Motion detection,Computer science,Meta-optimization,Workstation,Real-time computing,Estimation theory,Visual surveillance,Genetic algorithm,Generality,Calibration
Journal
Volume
Issue
ISSN
23
9
Image and Vision Computing
Citations 
PageRank 
References 
2
0.37
9
Authors
1
Name
Order
Citations
PageRank
Alessandro Bevilacqua120026.45