Single camera lane detection and tracking

Schreiber, David; Alefs, B.; Clabian, Markus

doi:10.1109/itsc.2005.1520207

Cited by 53 publications

(44 citation statements)

References 5 publications

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“…For comparison purposes, we implemented the Hough transform-based method (HT), which is commonly used in lane detection, e.g. [7]. Another method used for the comparison is the work of Lee and Cho [5] using colour information to extract lane marker pixels and edge orientation information to estimate lane boundaries.…”

Section: Resultsmentioning

confidence: 99%

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Study on Surface Roughness and Friction during Hot Rolling of Stainless Steel 301

Wei

Jiang

Huang

et al. 2012

AMR

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Study on surface roughness and friction during hot rolling of stainless steel 301 AbstractA well-defined boundary condition is important for generating an accurate model for simulating metal forming process. It is important to characterize the features of the oxide scale in hot rolling of stainless steel strip. Short time oxidation tests in humid air with water vapor content of 7.0 vol. % were carried out using Gleeble 3500 thermo-mechanical simulator. The deformation, surface morphology of oxide scale, and the friction in hot rolling were studied by conducting hot rolling tests. The results show that the surface roughness decreases with an increase of reduction. The effect of oxide scale on friction and surface roughness transfer in hot rolling depends on oxide scale generated during reheating. When reheating time is increased, the average thickness of oxide scale increases and a relatively rough surface was obtained after hot rolling. Thick oxide scale of 301 steel shows the high lubricative effect.

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Section: Resultsmentioning

confidence: 99%

“…straight lines, B-Splines, parabola, or hyperbola) to identify the lane markers. Examples of this approach include [8,7,6]. The edge-based approach is simple to implement and has less computational complexity.…”

Section: Introductionmentioning

confidence: 99%

Study on Surface Roughness and Friction during Hot Rolling of Stainless Steel 301

Wei

Jiang

Huang

et al. 2012

AMR

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“…(12) 1 The position of the vanishing point can be approximated either manually or automatically [16]. For the experiment purpose, we manually estimated the vanishing point.…”

Section: Linear Fractional Transformationmentioning

confidence: 99%

Vehicle Tracking Using Projective Particle Filter

Bouttefroy

Bouzerdoum

Phung

et al. 2009

2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance

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This article introduces a new particle filtering approach for object tracking in video sequences. The projective particle filter uses a linear fractional transformation, which projects the trajectory of an object from the real world onto the camera plane, thus providing a better estimate of the object position. In the proposed particle filter, samples are drawn from an importance density integrating the linear fractional transformation. This provides a better coverage of the feature space and yields a finer estimate of the posterior density. Experiments conducted on traffic video surveillance sequences show that the variance of the estimated trajectory is reduced, resulting in more robust tracking.

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“…Other research groups use Hough transform to directly detect vanishing point [13]. In this case all edges on images are involved in vanishing point generation and therefore this approach is too computational heavy for our hardware constraints.…”

Section: A Pitch Detectormentioning

confidence: 99%

Obstacle detection and classification fusing radar and vision

Bertozzi

Bombini

Cerri

et al. 2008

2008 IEEE Intelligent Vehicles Symposium

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Abstract-This paper presents a system whose aim is to detect and classify road obstacles, like pedestrians and vehicles, by fusing data coming from different sensors: a camera, a radar, and an inertial sensor. The camera is mainly used to refine the vehicles' boundaries detected by the radar and to discard those who might be false positives; at the same time, a symmetry based pedestrian detection algorithm is executed, and its results are merged with a set of regions of interest, provided by a Motion Stereo technique.The tests have been performed in several environments and traffic situations, their results showed how the vision based filtering provides an effective reduction of radar's false positives; furthermore, the regions of interest detected by the Motion Stereo algorithm, truly improves the pedestrian detector's performance again by keeping low the number of detection errors.The system has been shown during the APALACI-PReVENT European IP final demonstration 1 in September 2007 in Versailles (France).

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Single camera lane detection and tracking

Cited by 53 publications

References 5 publications

Study on Surface Roughness and Friction during Hot Rolling of Stainless Steel 301

Study on Surface Roughness and Friction during Hot Rolling of Stainless Steel 301

Vehicle Tracking Using Projective Particle Filter

Obstacle detection and classification fusing radar and vision

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