Homograpphy from a vanishing point in urban scenes

Simond, Nicolas; Rives, Patrick

doi:10.1109/iros.2003.1250759

Cited by 24 publications

(22 citation statements)

References 10 publications

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“…For straight, flat roads, we set R(v) to be the entire image, minus edge pixels excluded from convolution by the kernel size, and minus pixels above the current candidate v. Only pixels below the vanishing line l implied by v are allowed to vote because support is only sought from features in the plane of the road (though in urban scenes out-of-plane building features, etc. may corroborate this decision [15]). In this work, we assume that l is approximately horizontal.…”

Section: Straight Roadsmentioning

confidence: 70%

“…A number of researchers have used vanishing points as global constraints for road following or identification of painted features on roads (such as so-called "zebra crossings," or crosswalks) [14,13,15,9,2]. Broadly, the key to the approach is to use a voting procedure like a Hough transform on edge-detected line segments to find points where many intersect.…”

Section: Introductionmentioning

confidence: 99%

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Texture-Based Vanishing Point Voting for Road Shape Estimation

Rasmussen

2004

Procedings of the British Machine Vision Conference 2004

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Many rural roads lack sharp, smoothly curving edges and a homogeneous surface appearance, hampering traditional vision-based road-following methods. However, they often have strong texture cues parallel to the road direction in the form of ruts and tracks left by other vehicles. This paper describes an unsupervised algorithm for following ill-structured roads in which dominant texture orientations computed with Gabor wavelet filters vote for a consensus road vanishing point location. The technique is first described for estimating the direction of straight-road segments, then extended to curved and undulating roads by tracking the vanishing point indicated by a differential "strip" of voters moving up toward the nominal vanishing line. Finally, the vanishing point is used to constrain a search for the road boundaries by maximizing texture-and color-based region discriminant functions. Results are shown for a variety of road scenes including gravel roads, dirt trails, and highways.

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Section: Straight Roadsmentioning

confidence: 70%

Section: Introductionmentioning

confidence: 99%

Texture-Based Vanishing Point Voting for Road Shape Estimation

Rasmussen

2004

Procedings of the British Machine Vision Conference 2004

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“…We detailed in a precedent article [13] how to extract the road plane boundaries in images from edges provided by a Canny edge detector. We introduced the Dominant Vanishing Point which is the intersection between all the 3D lines aligned with the road direction and the retinal plane.…”

Section: A Segmentation Of the Road Planementioning

confidence: 99%

Reconstruction of the road plane with an embedded stereo-rig in urban environments

Simond

2006 IEEE Intelligent Vehicles Symposium

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Abstract-We present in this article an original method to reconstruct the road in the specific context of urban environment thanks to the data provided by an uncalibrated stereo-vision system. The method consists on extracting then tracking features (points, lines) from the road and estimate the homography induced by the plane between two poses. The purposed method copes with the dense traffic conditions: the free space required (first ten meters in front of the vehicle) is slightly equivalent to the security distance between two vehicles. Experimental issues from real data are presented and discussed.

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“…A number of researchers have used vanishing points as global constraints for road following or identification of painted features on roads (such as so-called "zebra crossings," or crosswalks) [9,10,11,12]. Broadly, the key to the approach is to use a voting procedure like a Hough transform on edge-detected line segments to find points where many intersect.…”

Section: Introductionmentioning

confidence: 99%

Grouping dominant orientations for ill-structured road following

Rasmussen

Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004.

168

138

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Many rural roads lack sharp, smoothly curving edges and a homogeneous surface appearance, hampering traditional vision-based road-following methods. However, they often have strong texture cues parallel to the road direction in the form of ruts and tracks left by other vehicles. In this paper, we describe an algorithm for following ill-structured roads in which dominant texture orientations computed with multi-scale Gabor wavelet filters vote for a consensus road vanishing point location. In-plane road curvature and out-of-plane undulation are estimated in each image by tracking the vanishing point indicated by a horizontal image strip as it moves up toward the putative vanishing line. Particle filtering is also used to track the vanishing point sequence induced by road curvature from image to image. Results are shown for vanishing point localization on a variety of road scenes ranging from gravel roads to dirt trails to highways.

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Homograpphy from a vanishing point in urban scenes

Cited by 24 publications

References 10 publications

Texture-Based Vanishing Point Voting for Road Shape Estimation

Texture-Based Vanishing Point Voting for Road Shape Estimation

Reconstruction of the road plane with an embedded stereo-rig in urban environments

Grouping dominant orientations for ill-structured road following

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