[1]ZHANG Tao,FEI Shu-min,LI Xiao-dong.Vehicle recognition using boundary invariants and a genetic algorithm trained radial basis function neural network[J].CAAI Transactions on Intelligent Systems,2009,4(3):278-272.
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Vehicle recognition using boundary invariants and a genetic algorithm trained radial basis function neural network

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 4 Number of periods: 2009 3 Page number: 278-272 Column: 学术论文—机器感知与模式识别 Public date: 2009-06-25
Title:
Vehicle recognition using boundary invariants and a genetic algorithm trained radial basis function neural network
Author(s):
ZHANG Tao;  FEI Shu-min;  LI Xiao-dong
Key Laboratory of Measurement and Control of Complex Systems of Engineering of Ministry of Education, Southeast University, Nanjing 210096, China
Keywords:
vehicle recognition;  genetic algorithms;  radial basis function neural network;  boundary invariant moments
CLC:
TP391.41
DOI:
-
Abstract:
A method for vehicle recognition using the modified boundary invariant moments and a genetic algorithm trained radial basis function (GARBF) neural network was developed. The modified boundary invariant moments have the accustomed invariance for rotation, scaling and translation of targets, which can be used as the invariant characteristic vectors. Using these features as the inputs of a neural network, the vehicle targets can then be recognized accurately. In order to improve recognition accuracy and speed, the genetic algorithm (GA) was used to optimize the RBF parameters: centers, variance, and numbers of hidden nodes. Experimental results indicated that this method, which introduces invariants based on boundaries, yields robust target recognition with greatly reduced computation time and improved efficiency.
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