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Fusing Multiple Features to Detect On-road Vehicles

CAI Yi-hong(College of Information Science and Engineering Hunan University,Changsha 410082,China)  
Improving cascade classifier based on Haar-like feature and Adaboost,this paper proposes an on-road vehicle detection method fusing Harr-like and HOG.Firstly,HOG feature is integrated into the traditional Haar-like feature set.Additionally,different weak classifiers for HOG features and Haar-like features are designed,and Gentle Adaboost algorithm is adopted to train the layer classifiers.Finally,based on the fusion features,a cascade classifier combined with Support Vector Machine is proposed.In the last few layers of the cascade,feature vectors composed by the features that selected by Gentle Adaboost algorithm are used to train robust SVM classifiers.Experimental results indicate that the proposed method can detect on-road vehicles effectively.
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