Robust Vehicle Detection Algorithm Based on Co-training Method

To improve the adaptability of existing vehicle detection algorithms in complex traffic circumstances,a robust detection algorithm based on co-training from semi-supervised learning methods was proposed.First,according to a small number of humanly labeled samples,two classifiers were trained,which were AdaBoost based on Haar-like features and the SVM(support vector machines) based on HOG(histograms of oriented gradients) features,respectively,so that both of them had some identification ability.Second,on the basis of co-training from semi-supervised learning framework,the new samples gained from the two algorithms above were added to mutual sample sets to increase the number of training samples,and the train was repeated.Due to the redundancy these two features had,the detected positive and negative samples would contain the images which were missed out or falsely detected mutually.Because of the increasing number of samples,the robustness of the new re-training classifiers has been greatly improved so that the classifiers can detect the vehicles accurately.Besides,there will be no need to mark artificially,but to classify and mark the unlabeled samples by the algorithms.Therefore,it can highly improve the adaptability of vehicle detection algorithm.