Road tracking, lane segmentation and obstacle recognition by mathematical morphology

Yu, Xiangzhan; Beucher, Serge; Bilodeau, Michel

doi:10.1109/ivs.1992.252251

Cited by 30 publications

(13 citation statements)

References 7 publications

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“…Other authors analyse intensity histograms to find a threshold separating lane markings from the background (Kluge and Johnson, 1995;Gonzalez and Ozguner, 2000). Morphological filters are used by Broggi (1995) and Yu et al (1992). Colour images are also used for this purpose.…”

Section: Road Markings From Pixel and Region Classificationmentioning

confidence: 99%

3D road marking reconstruction from street-level calibrated stereo pairs

Soheilian

Paparoditis

Boldo

2010

ISPRS Journal of Photogrammetry and Remote Sensing

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Section: Road Markings From Pixel and Region Classificationmentioning

confidence: 99%

3D road marking reconstruction from street-level calibrated stereo pairs

Soheilian

Paparoditis

Boldo

2010

ISPRS Journal of Photogrammetry and Remote Sensing

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“…The techniques implemented in the previously mentioned systems range from the determination of the characteristics of painted lane markings [30] eventually aided by color information [19] to the use of deformable templates (such as LOIS [31], DBS [7], or ARCADE [29]), to an edge-based recognition using a morphological paradigm [3], [5], [59], to a model-based approach (as implemented in VaMoRs [26] or SCARF [17]). A model-based analysis of road markings has also been used to perform the analysis of intersections in city traffic images [21], [32]; nevertheless, as discussed in [46], the use of a model-based search approach has several drawbacks, such as the problem of using and maintaining an appropriate geometrical road model, the difficulty in detecting and matching complex road features, and the complexity of the computations involved.…”

Section: A Lane Detectionmentioning

confidence: 99%

GOLD: a parallel real-time stereo vision system for generic obstacle and lane detection

Bertozzi

Broggi

1998

IEEE Trans. on Image Process.

893

404

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Abstract-This paper describes the Generic Obstacle and Lane Detection system (GOLD), a stereo vision-based hardware and software architecture to be used on moving vehicles to increment road safety. Based on a full-custom massively parallel hardware, it allows to detect both generic obstacles (without constraints on symmetry or shape) and the lane position in a structured environment (with painted lane markings) at a rate of 10 Hz. Thanks to a geometrical transform supported by a specific hardware module, the perspective effect is removed from both left and right stereo images; the left is used to detect lane markings with a series of morphological filters, while both remapped stereo images are used for the detection of free-space in front of the vehicle. The output of the processing is displayed on both an onboard monitor and a control-panel to give visual feedbacks to the driver. The system was tested on the mobile laboratory (MOB-LAB) experimental land vehicle, which was driven for more than 3000 km along extra-urban roads and freeways at speeds up to 80 km/h, and demonstrated its robustness with respect to shadows and changing illumination conditions, different road textures, and vehicle movement.

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“…Beucher and his colleagues [19,5] worked on road segmentation and obstacle detection based on watersheds. Their techniques consist of applying a temporal filter for noise reduction (and connection of ground markings), followed by edge detection and watershed segmentation.…”

Section: Related Workmentioning

confidence: 99%