Crosswalk Detection Using Dynamic Bayesian Network Based on Analyzing Edge Pattern
Mirai Higuchi, Takeshi Shima, Shoji Muramatsu, Kota Irie, Tatsuhiko Monji · IEEJ Transactions on Electronics Information and Systems · 2010
This paper proposes a novel crosswalk detection technique for self-localization of automobile. The self-localization function based on the proposed technique can estimate highly accurate self-position by comparing results of image recognition with positions of crosswalks in map database. This paper focuses on a robust method to detect a crosswalk and its reference point which is used to calculate the distance between the self-position of host vehicle and the crosswalk. Our method can detect crosswalks and reference points from rear camera image sequences in real time. The previous road marking detection techniques hardly detect crosswalks with robustness because the rear camera images have some noise such as damages of road markings, halation, and shadows. Our method estimates the state including rough relative position of the crosswalk by Dynamic Bayesian Network in order to detect crosswalks and reference points robustly. The proposed method uses also the specification of crosswalk to reduce computational cost. The proposed method was tested on real images to confirm the accuracy and computational cost. The experimental results show that our method can detect crosswalks with a high degree of stability in real time.