Automatic detection of zebra crossings from mobile LiDAR data

Riveiro, Belén; González-Jorge, H.; Martínez-Sánchez, J.; Díaz-Vilariño, L.; Arias, Pedro

doi:10.1016/j.optlastec.2015.01.011

Cited by 76 publications

(57 citation statements)

References 15 publications

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“…Figure 5 shows a rendering of the scene, in which the arrangement of luminaires and horizontal road signs are presented. Horizontal signs were obtained using automatic detection algorithms developed by Riveiro [39].…”

Section: Road Street Lighting Facilitymentioning

confidence: 99%

Maintenance Factor Identification in Outdoor Lighting Installations Using Simulation and Optimization Techniques

2018

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This document addresses the development of a novel methodology to identify the actual maintenance factor of the luminaires of an outdoor lighting installation in order to assess their lighting performance. The method is based on the combined use of Radiance, a free and open-source tool, for the modeling and simulation of lighting scenes, and GenOpt, a generic optimization program, for the calibration of the model. The application of this methodology allows the quantification of the deterioration of the road lighting system and the identification of luminaires that show irregularities in their operation. Values lower than 9% for the error confirm that this research can contribute to the management of street lighting by assessing real road conditions.

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Section: Road Street Lighting Facilitymentioning

confidence: 99%

Maintenance Factor Identification in Outdoor Lighting Installations Using Simulation and Optimization Techniques

2018

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“…Crosswalks can be detected on roads by analysing point cloud intensity (Riveiro et al, 2015), which is an additional information still scarcely used in point cloud segmentation and classification, but able to provide relevant support (Crosilla et al, 2014). As traffic and road signs have a high value of intensity in comparison with the rest of points in the point cloud, they may be easily detected.…”

Section: Overview Of Urban Ground Classificationmentioning

confidence: 99%

“…Balado et al (2017b) classify urban ground elements in point clouds (sidewalks, roads, curbs and stairs) with a high level of detail. Riveiro et al (2015) detect zebra crossings, what is very relevant to locate the areas where a pedestrian may cross roads without endangering his life and cause traffic accidents.…”

Section: Introductionmentioning

confidence: 99%

Pedestrian Pathfinding in Urban Environments: Preliminary Results

López-Pazos

Balado

Díaz-Vilariño

et al. 2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:With the rise of urban population, many initiatives are focused upon the smart city concept, in which mobility of citizens arises as one of the main components. Updated and detailed spatial information of outdoor environments is needed to accurate path planning for pedestrians, especially for people with reduced mobility, in which physical barriers should be considered. This work presents a methodology to use point clouds to direct path planning. The starting point is a classified point cloud in which ground elements have been previously classified as roads, sidewalks, crosswalks, curbs and stairs. The remaining points compose the obstacle class. The methodology starts by individualizing ground elements and simplifying them into representative points, which are used as nodes in the graph creation. The region of influence of obstacles is used to refine the graph. Edges of the graph are weighted according to distance between nodes and according to their accessibility for wheelchairs. As a result, we obtain a very accurate graph representing the asbuilt environment. The methodology has been tested in a couple of real case studies and Dijkstra algorithm was used to pathfinding. The resulting paths represent the optimal according to motor skills and safety.

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“…The direction and size of the zebra crossing is computed from the angle defined by lines and their length, respectively. Further details of the method can be seen in Riveiro et al, 2015.…”

Section: Line Classification Using Hough Transformmentioning

confidence: 99%

Simple Approaches to Improve the Automatic Inventory of Zebra Crossing From MLS Data

Arias

Riveiro

Soilán

et al. 2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The city management is increasingly supported by information technologies, leading to paradigms such as smart cities, where decision-makers, companies and citizens are continuously interconnected. 3D modelling turns of great relevance when the city has to be managed making use of geospatial databases or Geographic Information Systems. On the other hand, laser scanning technology has experienced a significant growth in the last years, and particularly, terrestrial mobile laser scanning platforms are being more and more used with inventory purposes in both cities and road environments. Consequently, large datasets are available to produce the geometric basis for the city model; however, this data is not directly exploitable by management systems constraining the implementation of the technology for such applications. This paper presents a new algorithm for the automatic detection of zebra crossing. The algorithm is divided in three main steps: road segmentation (based on a PCA analysis of the points contained in each cycle of collected by a mobile laser system), rasterization (conversion of the point cloud to a raster image coloured as a function of intensity data), and zebra crossing detection (using the Hough Transform and logical constrains for line classification). After evaluating different datasets collected in three cities located in Northwest Spain (comprising 25 strips with 30 visible zebra crossings) a completeness of 83% was achieved.

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Automatic detection of zebra crossings from mobile LiDAR data

Cited by 76 publications

References 15 publications

Maintenance Factor Identification in Outdoor Lighting Installations Using Simulation and Optimization Techniques

Maintenance Factor Identification in Outdoor Lighting Installations Using Simulation and Optimization Techniques

Pedestrian Pathfinding in Urban Environments: Preliminary Results

Simple Approaches to Improve the Automatic Inventory of Zebra Crossing From MLS Data

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