Crosswalk Detection Based on MSER and ERANSAC

Crosswalks detection and location in traffic scenes plays an important role in the Intelligent Transportation Management System (ITMS). In this paper, we propose a new novel and robust approach for the detection and location of crosswalks, which is based on Maximally Stable Extremal Regions (MSER) and extended Random Sample Consensus (ERANSAC). Specially, first, the method with temporal median of background extraction is applied to traffic monitoring videos so that the foreground has a less disturbance for crosswalks detection and location. Second, we use a method of MSER to extract crosswalk features in the image. Last, we invent the ERANSAC to pick out crosswalk features extracted by MSER to detect and locate crosswalk and indicate the position of crosswalk in the image. Compared to the existing detection methods, the proposed method based on MSER can efficiently extract crosswalk regions under various illumination conditions, which can avoid the selection of thresholds according to the current environment situation and greatly improve the system flexibility and robustness. The ERANSAC can determine the distance between candidate samples and get the principle components by the way of eliminating the non-crosswalk regions. Promising experimental results have demonstrated that the proposed method has a high detection rate at extremely false alarm and is robust to the different crosswalk angle and various illuminations.