Traffic sign detection based on co-training method

To improve the performance of traffic sign detection and recognition systems in real implementation for the outdoor challenging environment, we propose a robust traffic sign detection algorithm based on co-training learning methods with a small number of manually labeled initial samples (opposite to collect all possible views) in this paper. With consideration on the various appearances of different traffic signs in real environment, two kinds of redundant textual descriptors are extracted for reinforcing the discrimination ability of traffic sign detection classifier from background. First, a novel traffic sign candidate regions extraction method is used based on probability map image built from multiple color-histogram back-projection. Secondly, a small number of labeled samples are used to train two classifiers respectively: one is AdaBoost with MB-LBP (multi-block local binary pattern) features and the other is SVM (support vector machines) with HOG (histograms of oriented gradients) features. Then, on the basis of co-training semi-supervised learning framework, the newly labeled samples with higher confidence from one classifier are used to update the training samples of the other one. Because of the constant increment of each training samples, the performance of traffic sign detection is highly improved which is evaluated intensively in the results of our experiment.