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

Simond, Nicolas

doi:10.1109/ivs.2006.1689607

Cited by 12 publications

(11 citation statements)

References 15 publications

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“…Therefore several works have been proposed that aim at estimating the ground plane as the basis for a posterior object detection on that plane [1][2][3]. Usually, they use a calibrated stereo rig that allows for a direct estimation of the ground region [4,5]. Nonetheless, stereo systems have a number of disadvantages compared to monocular systems, especially in terms of cost and flexibility.…”

Section: Introductionmentioning

confidence: 99%

“…odometers, speedometers, accelerometers, gyroscopes) has been used in numerous works to facilitate ego-motion estimation [6,7]. However, this information may not be available in all applications, and even if available it might be inaccurate because of drifts [5,8], thus it is interesting and challenging to capitalise on all the information attainable via pure vision analysis. Unfortunately, visual ego-motion estimation is a complex task and still an active research line [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

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Homography-based ground plane detection using a single on-board camera

Arróspide

Salgado

Nieto

et al. 2010

IET Intell. Transp. Syst.

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This study presents a robust method for ground plane detection in vision-based systems with a nonstationary camera. The proposed method is based on the reliable estimation of the homography between ground planes in successive images. This homography is computed using a feature matching approach, which in contrast to classical approaches to on-board motion estimation does not require explicit ego-motion calculation. As opposed to it, a novel homography calculation method based on a linear estimation framework is presented. This framework provides predictions of the ground plane transformation matrix that are dynamically updated with new measurements. The method is specially suited for challenging environments, in particular traffic scenarios, in which the information is scarce and the homography computed from the images is usually inaccurate or erroneous. The proposed estimation framework is able to remove erroneous measurements and to correct those that are inaccurate, hence producing a reliable homography estimate at each instant. It is based on the evaluation of the difference between the predicted and the observed transformations, measured according to the spectral norm of the associated matrix of differences. Moreover, an example is provided on how to use the information extracted from ground plane estimation to achieve object detection and tracking. The method has been successfully demonstrated for the detection of moving vehicles in traffic environments.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Homography-based ground plane detection using a single on-board camera

Arróspide

Salgado

Nieto

et al. 2010

IET Intell. Transp. Syst.

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show abstract

“…These features extraction methods are often based on corners, edges, ridges, colours and textures of the image [5][6][7][8][9][10][11][12][13][14]. For example, Rasmussen et al [12] use information about texture orientations at pixels, obtained using Gabor filters, to detect the road.…”

Section: Introductionmentioning

confidence: 99%

Multiple Condensation filters for road detection and tracking

Wang

Fairhurst

2010

Pattern Anal Applic

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In this paper, we present a novel and robust road tracking system for vision-based personal navigation. Novelty of the work includes the use of multiple Condensation filters to track the road of arbitrary shape and automatic switching between trackers according to road conditions. The approach allows the road to be represented as a simple hyperbola. It also supports the representation of the road as a sequence of connected arcs/segments so that information from a digital map can be integrated into tracking. The parameters of the hyperbola road model are estimated using multiple vanishing points located in image strips. The road tracking method is robust in dealing with complex road shapes, background clutters, shadows, and road markings. Experiments using real videos demonstrate the robustness of our approach.

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“…These models are then instantiated with features extracted from images of the road, such as corners [8], edges [9], ridges [10], color [11], [12], texture [13], [14], and hybrid features [15], [16].…”

Section: Introductionmentioning

confidence: 99%

Robust Road Modeling and Tracking Using Condensation

Wang

Fairhurst

2008

IEEE Trans. Intell. Transport. Syst.

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Abstract-In this paper, we present a robust road detection and tracking method based on a condensation particle filter for real-time video-based navigation applications. The image is divided into horizontal strips, and vanishing point (VP) detection is performed on each image strip. We propose a method for estimating the density of road boundary line segments in the image so that VP detection in an image strip takes into account the detection results in the neighboring image strips. This use of contextual information for VP detection leads to more accurate detection results. The estimated road parameters are then used to initialize the condensation tracker. Experiments using real road videos demonstrate the robustness of our method to difficult road conditions due to the presence of partial occlusion, shadows, and road signs.

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

Cited by 12 publications

References 15 publications

Homography-based ground plane detection using a single on-board camera

Homography-based ground plane detection using a single on-board camera

Multiple Condensation filters for road detection and tracking

Robust Road Modeling and Tracking Using Condensation

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