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Traffic Sign Recognition Using Extreme Learning Classifier with Deep Convolutional Features

Traffic sign recognition is an important but challenging task, especially for automated driving and driver assistance. Its accuracy depends on two aspects: feature extractor and classifier. Current popular algorithms mainly use convolutional neural networks (CNN) to execute both feature extraction and classification. Such methods could achieve impressive results but usually on the basis of an extremely huge and complex network. What’s more, since the fullyconnected layers in CNN form a classical neural network classifier, which is trained by gradient descent-based implementations, the generalization ability is limited and sub-optimal. The performance could be further improved if other favorable classifiers are used and extreme learning machine (ELM) classifier is just the candidate. In this paper, ELM classifier equipped with deep convolutional features is utilized, which integrates the terrific discriminative capability of deep convolutional features learnt by CNN with the outstanding generalization performance of ELM classifier. Firstly CNN learns deep and robust features, followed by the removing of the fully-connected layers, which turns CNN to be the feature extractor. Then ELM fed with the CNN features is used as the classifier to conduct an excellent classification. Experiments on German traffic sign recognition benchmark (GTSRB) demonstrate that the proposed method can obtain competitive results with state-of-the-art algorithms with much less complexity.