AdaBoost-Bagging vehicle detection algorithm based on multi-mode weak classifier

Focusing on the problem that the vehicle detection rate of existed vehicle detection algorithms is lower in real complex road environment, a vehicle detection algorithm was proposed, in which multi-model weak classifiers were integrated into strong classifier by using AdaBoost-Bagging method.In the algorithm, discriminative model could generate a fine decision boundary by using more features, and generative model could eliminate negative examples by using fewer features.To combine the advantages of discriminative model and generative model, discriminative classifier with Haar feature and generative classifier with HOG feature were built.Combined with AdaBoost algorithm, AdaBoost-Bagging strong classifier was obtained by using Bagging algorithm that is an integrated learning algorithm with strong generalization ability.Vehicle detection algorithm was tested based on Caltech1999 dataset and real road images.Test result indicates that compared with sole mode weak classifier, AdaBoost-Bagging strong classifier maintains superiority in classification ability and processing time, its high detection rate and low false detection rate are 95.7%, 0.000 27% respectively, and the detection time of each frame is 25 ms that is less.Compared with the traditional cascade AdaBoost classifier, the detection time of the AdaBoost-Bagging strong classifier increases 12%, the training time increases 30%, but the detection rate increases 1.8%, and the false detection rate decreases 0.000 06%.The proposed algorithm is better than other vehicle detection algorithms, including Haar featurebased AdaBoost classifier, HOG feature-based SVM classifier, HOG feature-based DPM classifier, and has better vehicle detection effect.