Mobile mapping system for the automated detection and analysis of road delineation

Murray, S.; Haughey, S.; Brogan, Michael; Fitzgerald, Conor; McLoughlin, Simon; Deegan, Catherine

doi:10.1049/iet-its.2010.0105

Cited by 19 publications

(5 citation statements)

References 14 publications

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“…Specifically, it is even greatly dangerous to work on highways, overhead roads, or in tunnels. Recently, mobile mapping systems (MMS) mounted with digital camera(s) or video camera(s) [9], [10] have emerged as an effective, promising tool for a wide range of transportation-related activities. However, optical imaging-based MMS suffer greatly from environmental illumination conditions, thereby limiting mapping missions to only the daytime.…”

mentioning

confidence: 99%

Bag of Contextual-Visual Words for Road Scene Object Detection From Mobile Laser Scanning Data

Guan

et al. 2016

IEEE Trans. Intell. Transport. Syst.

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This paper proposes a novel algorithm for detecting road scene objects (e.g., light poles, traffic signposts, and cars) from 3-D mobile-laser-scanning point cloud data for transportationrelated applications. To describe local abstract features of point cloud objects, a contextual visual vocabulary is generated by integrating spatial contextual information of feature regions. Objects of interest are detected based on the similarity measures of the bag of contextual-visual words between the query object and the segmented semantic objects. Quantitative evaluations on two selected data sets show that the proposed algorithm achieves an average recall, precision, quality, and F-score of 0.949, 0.970, 0.922, and 0.959, respectively, in detecting light poles, traffic signposts, and cars. Comparative studies demonstrate the superior performance of the proposed algorithm over other existing methods.

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confidence: 99%

Bag of Contextual-Visual Words for Road Scene Object Detection From Mobile Laser Scanning Data

Guan

et al. 2016

IEEE Trans. Intell. Transport. Syst.

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“…In addition, it is greatly dangerous for the workers to work on highways or in tunnels. As a revolutionary progress, mobile mapping systems, mounted with digital cameras or videos [5], [6], have been developed and extensively used in a variety of transportation-related activities. However, such systems, using optical imaging, suffer greatly from distortions, environmental illumination conditions, occlusions caused by pedestrians and vehicles, shadows cast by buildings and trees, and lack of accurate georeferencing.…”

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confidence: 99%

Automated Extraction of Urban Road Facilities Using Mobile Laser Scanning Data

Guan

et al. 2015

IEEE Trans. Intell. Transport. Syst.

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This paper proposes a novel, automated algorithm for rapidly extracting urban road facilities, including street light poles, traffic signposts, and bus stations, for transportation-related applications. A detailed description and implementation of the proposed algorithm is provided using mobile laser scanning data collected by a state-of-the-art RIEGL VMX-450 system. First, to reduce the quantity of data to be handled, a fast voxel-based upward growing method is developed to remove ground points. Then, off-ground points are clustered and segmented into individual objects via Euclidean distance clustering and voxel-based normalized cut segmentation, respectively. Finally, a 3-D object matching framework, benefiting from a locally affine-invariant geometric constraint, is developed to achieve the extraction of 3-D objects. Quantitative evaluations show that the proposed algorithm attains an average completeness, correctness, quality, and F 1 -measure of 0.949, 0.971, 0.922, and 0.960, respectively, in extracting 3-D light poles, traffic signposts, and bus stations. Comparative studies demonstrate the efficiency and feasibility of the proposed algorithm for automated and rapid extraction of urban road facilities.Index Terms-Bus station, light pole, mobile laser scanning (MLS), road feature inventory, traffic safety, traffic signpost.

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“…It is capable of acquiring massive and meticulous spatial information in a highly efficient manner. Many studies have utilized this novel technique in collecting required information for transportation applications (Ishikawa et al 2007;Murray et al 2011;Yang, Fang, and Li 2013). The 3D spatial data can be obtained directly using the point clouds from a laser scanner or stereo images from dual cameras on an MMS.…”

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confidence: 99%

Mapping road surface features using single-camera images acquired by a mobile mapping system

Han

Chou

Tsai

2015

Journal of the Chinese Institute of Engineers

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Mapping road surface features, such as manholes, traffic markings, and cracks, is an essential task for transportation facility management. Although, these features can be rapidly surveyed using the latest mobile mapping techniques, a sophisticated sensor system with a complicated post-processing procedure is usually required. In this study, an efficient framework for modeling road surface features is proposed using a single camera system installed on a moving platform. First, the road surface images along a route of interest are acquired and potential objects are identified based on their shapes and recorded spectra in the images. Then, the contour pixels of the identified objects are extracted by the Canny edge detection technique. Finally, the 3D coordinates of the detected features in object space are obtained by integrating the profile-image technique and the instantaneous exterior orientation parameters of the platform. Based on the numerical results from a case study, it has been demonstrated that a fully automatic and reliable extraction of road surface features can be easily achieved by implementing the proposed approach. Consequently, the modeling of road surface features, which essentially contributes to the management of transportation facilities, can be executed in a cost-efficient manner.

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Mobile mapping system for the automated detection and analysis of road delineation

Cited by 19 publications

References 14 publications

Bag of Contextual-Visual Words for Road Scene Object Detection From Mobile Laser Scanning Data

Bag of Contextual-Visual Words for Road Scene Object Detection From Mobile Laser Scanning Data

Automated Extraction of Urban Road Facilities Using Mobile Laser Scanning Data

Mapping road surface features using single-camera images acquired by a mobile mapping system

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