Recognition and Reconstruction of Zebra Crossings on Roads from Mobile Laser Scanning Data

Abstract

Zebra crossings provide guidance and warning to pedestrians and drivers, thereby playing an important role in traffic safety management. Most previous studies have focused on detecting zebra stripes but have not provided full information about the areas, which is critical to both driver assistance systems and guide systems for blind individuals. This paper presents a stepwise procedure for recognizing and reconstructing zebra crossings using mobile laser scanning data. First, we propose adaptive thresholding based on road surface partitioning to reduce the impact of intensity unevenness and improve the accuracy of road marking extraction. Then, dispersion degree filtering is used to reduce the noise. Finally, zebra stripes are recognized according to the rectangular feature and fixed size, which is followed by area reconstruction according to arrangement patterns. We test our method on three datasets captured by an Optech Lynx mobile mapping system. The total recognition rate of 90.91% demonstrates the effectiveness of the method.

A Density-Based Clustering Method for Urban Scene Mobile Laser Scanning Data Segmentation

“…To select these targets, the ground voxels need to first be detected. Numerous researchers have focused their effort on the detection or even reconstruction of roads from point cloud data [2,[6][7][8][9][10][34][35][36]. In addition, roads can sometimes play the role of ground to provide the relative position of the street furniture to the ground in situations where the ground is mainly even vertically along the trajectory.…”

“…Intensity has been a widely utilized cue in the segmentation of road markings and road signs [8][9][10][34][35][36][37][38][39][40][41]. Intensity values are crucial in these methods because road markings are usually made of special pavement marking material and have higher reflective ability than does the remainder of the road surface.…”

“…Moreover, MLS data are more efficiently acquired than TLS data, as the latter are collected by manually positioned systems. Many researchers have studied the use of MLS data in urban scenes intensively, including in road [6,7] and road marking [8][9][10] detection, building detection and reconstruction [11][12][13], pole-like object detection [3,14,15], tree detection and modeling [16][17][18], and urban scene segmentation and classification [19][20][21][22][23].…”

A Density-Based Clustering Method for Urban Scene Mobile Laser Scanning Data Segmentation

“…Intensity has been a widely utilized cue in the segmentation of road markings and road signs [8][9][10][34][35][36][37]. Intensity values are crucial in these methods because road markings are usually made of special pavement marking material and have higher reflective ability than does the remainder of the road surface.…”

“…Moreover, MLS data are more efficiently acquired than TLS data, as the latter are collected by manually positioned systems. Many researchers have studied the use of MLS data in urban scenes intensively, including in road [6,7] and road marking [8][9][10] detection, building detection and reconstruction [11][12][13], pole-like object detection [3,14,15], tree detection and modeling [16][17][18], and urban scene segmentation and classification [19][20][21][22][23].…”