A Road Extraction Method by an Active Contour Model with Inertia and Differential Features

Sawano, Hiroaki

doi:10.1093/ietisy/e89-d.7.2257

Cited by 22 publications

(14 citation statements)

References 10 publications

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“…Those reviewed above are parametric models. Another category is semi-parametric, which mainly consists of splines, such as B-Snake [105], Cubic splines [80], active contours [106], etc. The advantage of these models is that they are more flexible and can cover various road shapes.…”

Section: Lane Line Marking Detectionmentioning

confidence: 99%

Perception, Planning, Control, and Coordination for Autonomous Vehicles

et al. 2017

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Autonomous vehicles are expected to play a key role in the future of urban transportation systems, as they offer potential for additional safety, increased productivity, greater accessibility, better road efficiency, and positive impact on the environment. Research in autonomous systems has seen dramatic advances in recent years, due to the increases in available computing power and reduced cost in sensing and computing technologies, resulting in maturing technological readiness level of fully autonomous vehicles. The objective of this paper is to provide a general overview of the recent developments in the realm of autonomous vehicle software systems. Fundamental components of autonomous vehicle software are reviewed, and recent developments in each area are discussed.

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Section: Lane Line Marking Detectionmentioning

confidence: 99%

Perception, Planning, Control, and Coordination for Autonomous Vehicles

et al. 2017

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“…A recent lane and road boundary detection survey (Hillel et al, 2012) explored a large body of research on lane detection, including methods using gradientbased feature detection (Samadzadegan et al, 2006;Nieto et al, 2008;Sawano and Okada, 2006), steerable filters (McCall and Trivedi, 2006), box filters (Huang et al, 2009;Wu et al, 2008), and learningbased lane pattern recognition (Cheng et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

“…Lane models, such as straight lines (Kim, 2008;Pomerleau, 1995;Rasmussen and Korah, 2005), parabolic curves (Huang et al, 2009;McCall and Trivedi, 2006), semi-parametric formulations such as splines (Kim, 2008), or active contours (Sawano and Okada, 2006) are found in the literature. Different model-fitting methods have been adopted including RANSAC (Sawano and Okada, 2006), particle swarms (Zhou et al, 2005), energy-based optimization (Sawano and Okada, 2006), genetic algorithms (Samadzadegan et al, 2006), and more.…”

Section: Introductionmentioning

confidence: 99%

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Map-based Lane and Obstacle-free Area Detection

2014

Proceedings of the 9th International Conference on Computer Vision Theory and Applications

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Abstract:With the emergence of intelligent Advanced Driving Assistance Systems (i-ADAS), the need for effective detection of vehicular surroundings is considered a necessity. The effectiveness of such systems directly depends on their performance in various environments such as rural and urban roads, and highways. Most of the current lane detection techniques are not suitable for urban roads with complex lane shapes and frequent occlusions. We propose a map-based lane detection approach which can robustly detect the lanes in urban and rural environments, and highways. We also present an algorithm for detecting obstacle-free areas in detected lanes based on the stereo depth maps of driving scenes. Experiments show that our approach reliably detects lanes and obstacle free areas within them, even in case of partially occluded or worn-off lane markers.

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“…Fully automatic is undoubtedly the final goal of the object recognition and feature extraction of RS images, but due to the complexity and diversity of RS images, the automatic extraction of man-made ground objects like roads involves many aspects including computer vision, artificial intelligence, pattern recognition and image understanding. Over years of research on the automatic extraction of linear features like road and a number of discussions and efforts made by experts at home and abroad, there is still no completed system free of manual intervention, so some researchers believe that in the foreseeable future, the automatic extraction is unrealistic, while semi-automatic methods like template matching, Snake model are more realistic [4,5,6] .…”

Section: Introductionmentioning

confidence: 99%

A rapid algorithm of road boundary extraction in universal remote sensing images

Xiajun

Wang

2009

PIAGENG 2009: Image Processing and Photonics for Agricultural Engineering

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Road information in RS images has high application value in many fields. Much attention has been paid to road extraction in RS images in recent years, and a lot of algorithms have been developed. But most of algorithms are limited to certain type of RS images, or feature low rate of recognition, and so on. So far users still can't find a satisfactory method. In order to meet the demands on data acquisition of GIS application, we developed a new extraction algorithm on road border. In this method, all roads are classified into two categories: roads with complete information and roads with incomplete information. At beginning, binary image is achieved through simple pretreatment on the whole RS image. With the initial seed point given artificially, then the whole road borders will be searched out rapidly making use of the five-neighborhoods searching algorithm. Roads that can't be searched successfully in above steps are named as roads with incomplete information. To these roads we add certain extra processes and extract borders again.

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A Road Extraction Method by an Active Contour Model with Inertia and Differential Features

Cited by 22 publications

References 10 publications

Perception, Planning, Control, and Coordination for Autonomous Vehicles

Perception, Planning, Control, and Coordination for Autonomous Vehicles

Map-based Lane and Obstacle-free Area Detection

A rapid algorithm of road boundary extraction in universal remote sensing images

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