Image segmentation and pattern recognition for road marking analysis

Rebut, Julien; Bensrhair, Abdelaziz; Toulminet, G.

doi:10.1109/isie.2004.1571896

Cited by 24 publications

(10 citation statements)

References 10 publications

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“…The value of HOM is low for inhomogeneous images and has a relatively higher value for homogeneous images. (5) Entropy:…”

Section: Glcm Featuresmentioning

confidence: 99%

“…Zebra crossings, a type of pedestrian crossing, are used in many places around the world, which is crucial information that is often ignored in geographic data collection [3]. Recently, with the increasing demands of detailed road information stimulated by local accessibility analysis, pedestrian simulation and prediction, and navigation of driverless cars, the extraction of zebra crossings particularly for reconstruction purposes, is becoming an important research topic [4][5][6][7]. High-resolution aerial images are one of the most significant and popular data sources for geographic data retrieval [8][9][10][11][12], and provides further possibilities for zebra crossings extraction and reconstruction.…”

Section: Introductionmentioning

confidence: 99%

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Extraction and Reconstruction of Zebra Crossings from High Resolution Aerial Images

Sun

Zhang

Gao

et al. 2016

IJGI

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Abstract:In this paper, an automatic approach for zebra crossing extraction and reconstruction from high-resolution aerial images is proposed. In the extraction procedure, zebra crossings are extracted by the JointBoost classifier based on GLCM (Gray Level Co-occurrence Matrix) features and 2D Gabor Features. In the reconstruction procedure, a geometric parameter model based on spatial repeatability relationships is globally fitted to reconstruct the geometric shape of zebra crossings. Additionally, a group of representative experiments is conducted to test the proposed method under interfered conditions, such as zebra crossings covered by pedestrians, shadows and color fading. Furthermore, the performance of the proposed extraction method is compared with the template matching method. Finally, the results show the validation of our proposed method, both in the extraction and reconstruction of zebra crossings.

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“…The value of HOM is low for inhomogeneous images and has a relatively higher value for homogeneous images. (5) Entropy:…”

Section: Glcm Featuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Extraction and Reconstruction of Zebra Crossings from High Resolution Aerial Images

Sun

Zhang

Gao

et al. 2016

IJGI

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“…A first approach to detect the markings itself is described in [10] with the goal to detect corrupted marking segments. A stereo camera system was used to generate a birds-eye-view on which a morpholocial filter was applied to remove everything except the arrow tips.…”

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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“…Inverse Perspective Mapping (IPM) Transformations [14] can be used to generate a bird's eye view of the road which often simplifies marking detection. Dedicated marking extraction algorithms have also been proposed using morphological operations and steerable filters as in [15].…”

Section: Road Markingsmentioning

confidence: 99%

Global environment interpretation from a new Mobile Mapping System

Smadja

Ninot

Gavrilovic

2010

2010 IEEE Intelligent Vehicles Symposium

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We present in this article different algorithms designed for automatic road environment interpretation, from a large amount of data, acquired by embedded sensors. Road markings and traffic signs are extracted then interpreted by means of original image processing techniques whereas road geometry is computed through lidar range data analysis. The outputs of all these algorithms are gathered into a single tool, which manages a precise and global geographic database in order to produce advanced maps. These maps can further be used for intelligent navigation.

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Image segmentation and pattern recognition for road marking analysis

Cited by 24 publications

References 10 publications

Extraction and Reconstruction of Zebra Crossings from High Resolution Aerial Images

Extraction and Reconstruction of Zebra Crossings from High Resolution Aerial Images

Detecting symbols on road surface for mapping and localization using OCR

Global environment interpretation from a new Mobile Mapping System

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