Automatic Traffic Lane Detection for Mobile Mapping Systems

Land-based mobile mapping systems have yielded an enormous time saving in capturing road networks and their surrounding. However, the manual extraction of the road information from the mobile mapping data is still a time-consuming task. This paper presents a robust algorithm for automatic traffic lane detection in image sequences from mobile mapping systems. There are two innovations in this work. First, we introduce the Maximally Stable Extremal Region (MSER) detector for lane marking feature extraction, and derive a novel shape descriptor in an affine invariant manner to describe region shapes and a modified Scale Invariant Feature Transform (SIFT) descriptor to capture feature appearance characteristics. MSER features are more stable compared to edge points or line pairs, and hence provide robustness to lane marking variations in scale, lighting, viewpoint and shadows. Second, we use probabilistic Latent Semantic Analysis (pLSA) topic model for simultaneous lane recognition and localization. Experimental results on traffic sequences in VISAT™ mobile mapping data demonstrate the effectiveness and robustness of the proposed method.