Learning‐based method for lane detection using regionlet representation

Lane detection is an important enabling or enhancing technology for many intelligent applications. A marker line can be segmented into several image blocks, each of which contains lane marking in the centre. This study proposes a learning‐based method for lane localisation via detecting and grouping such image blocks. The authors model the marking class using regionlet representation, in which each image block is regarded as a region and is represented by a group of regionlets. A region feature composed of the features extracted from the regionlets contributes a weak classifier. A cascade structure detector is then trained for lane detection. At early stages, it rejects as many negatives as possible. Each layer of the cascade detector is a strong classifier, which consists of several weak classifiers. A real AdaBoost algorithm is adopted to select the most discriminative features and to train the classifiers. Since the use of regionlet features allows desired performance with only a few weak classifiers and the dimensionality of the features is significantly reduced by principal component analysis, the computational burden of their algorithm is much lower than other learning‐based methods. Experiment results demonstrate the computational efficiency and robustness of the method.