Abstract An approach for detection and classification of objects based on Gabor features and enhanced fisher discriminant model (EFM) is presented in this paper. Decomposed by Gabor filters, the dimensions of Gabor features of object images and models are very large. Principal component analysis (PCA) is used to extract the master components and reduce dimensions of Gabor features. Whether there are vehicle objects or not is primarily justified by the magnitude of the Gabor features. If candidate object is detected, EFM is carried out to compare its features to those of models to determine which one it belongs to-vehicles or back ground. The experiments prove the proposed arithmetic can get good results while reducing the feature dimensions. Furthermore, arithmetics for determining vehicle's number and positions are also discussed. And the experimental results also validate their feasibility.
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2006, Vol. 19 
