Detection and recognition of urban road markings using images

Foucher, Philippe; Sebsadji, Yazid; Tarel, Jean-Philippe; Charbonnier, Pierre; Nicolle, Philippe

doi:10.1109/itsc.2011.6082840

Cited by 65 publications

(46 citation statements)

References 12 publications

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“…In a multilane scenario the free space must be split in lanes and for this road markings are crucial. As explained in [42], a median filter is applied to the input image. The window size should be adjusted due to perspective.…”

Section: Road Markingsmentioning

confidence: 99%

A Hybrid Vision-Map Method for Urban Road Detection

Fernández

Llorca

Sotelo

2017

Journal of Advanced Transportation

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A hybrid vision-map system is presented to solve the road detection problem in urban scenarios. The standardized use of machine learning techniques in classification problems has been merged with digital navigation map information to increase system robustness. The objective of this paper is to create a new environment perception method to detect the road in urban environments, fusing stereo vision with digital maps by detecting road appearance and road limits such as lane markings or curbs. Deep learning approaches make the system hard-coupled to the training set. Even though our approach is based on machine learning techniques, the features are calculated from different sources (GPS, map, curbs, etc.), making our system less dependent on the training set.

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Section: Road Markingsmentioning

confidence: 99%

A Hybrid Vision-Map Method for Urban Road Detection

Fernández

Llorca

Sotelo

2017

Journal of Advanced Transportation

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“…Finally, the displacement ∆Cam N seen by the camera becomes as (5), what shows that the real displacement ∆d N is not correctly estimated due to the presence of the object. …”

Section: Depth Ambiguitymentioning

confidence: 99%

Monocular Height Estimation Method with 3 Degree-Of-Freedom Compensation of Road Unevennesses

Kaneko¹,

Yamamoto²

2017

Adv. sci. technol. eng. syst. j.

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“…In [12] a system was proposed which detects lane markings in a given rectangular region and compares the binarized pattern to stored templates. Fourcher et al [13] used the difference between a median filtered image and the original to extract lane marking borders. The result was compared to recorded templates with both, Hamming distance and histograms.…”

Section: Related Workmentioning

confidence: 99%

Detecting symbols on road surface for mapping and localization using OCR

Schreiber

Poggenhans

Stiller

2014

17th International IEEE Conference on Intelligent Transportation Systems (ITSC)

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Abstract-In this paper, we present a system to detect symbols on roads (e.g. arrows, speed limits, bus lanes and other pictograms) with a common monoscopic or stereoscopic camera system. No manual labeling of images is necessary since the exact definitions of the symbols in the legal instructions for road paintings are used. With those vector graphics an Optical Character Recognition (OCR) System is trained. If only a monoscopic camera is used, the vanishing point is estimated and an inverse perspective transformation is applied to obtain a distortion free top-view. In case of the stereoscopic camera setup, the 3D reconstruction is projected to a ground plane. TESSERACT, a common OCR system is used to classify the symbols. If odometry or position information is available, a spatial filtering and mapping is possible. The obtained information can be used on one side to improve localization, on the other side to provide further information for planning or generation of planning maps.

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Detection and recognition of urban road markings using images

Cited by 65 publications

References 12 publications

A Hybrid Vision-Map Method for Urban Road Detection

A Hybrid Vision-Map Method for Urban Road Detection

Monocular Height Estimation Method with 3 Degree-Of-Freedom Compensation of Road Unevennesses

Detecting symbols on road surface for mapping and localization using OCR

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