Road detection using support vector machine based on online learning and evaluation

Zhou, Shengyan; Gong, Jianya; Xiong, Guangming; Chen, Huiyan; Iagnemma, Karl

doi:10.1109/ivs.2010.5548086

Cited by 88 publications

(48 citation statements)

References 6 publications

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“…Machine learning methods are commonly used for this task. Some examples of those techniques are the Support Vector Machine (SVM) [32], Neural Networks (NN) [33], Bayes Classifier, decision trees (DT), Random Trees (RT), Extremely Randomized Trees (ERT), and boosting [34,35]. They receive a feature vector and the corresponding label for each pixel in the image.…”

Section: Featuresmentioning

confidence: 99%

A Hybrid Vision-Map Method for Urban Road Detection

Fernández

Llorca

Sotelo

2017

Journal of Advanced Transportation

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A hybrid vision-map system is presented to solve the road detection problem in urban scenarios. The standardized use of machine learning techniques in classification problems has been merged with digital navigation map information to increase system robustness. The objective of this paper is to create a new environment perception method to detect the road in urban environments, fusing stereo vision with digital maps by detecting road appearance and road limits such as lane markings or curbs. Deep learning approaches make the system hard-coupled to the training set. Even though our approach is based on machine learning techniques, the features are calculated from different sources (GPS, map, curbs, etc.), making our system less dependent on the training set.

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Section: Featuresmentioning

confidence: 99%

A Hybrid Vision-Map Method for Urban Road Detection

Fernández

Llorca

Sotelo

2017

Journal of Advanced Transportation

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“…Although good results were obtained with techniques like Support Vector Machines (SVM) (Zhou et al, 2010) or model-based object detectors (Felzenszwalb et al, 2010), a great improvement was made with the popularization of Convolutional Neural Networks (CNN) after the appearance of the AlexNet (Krizhevsky et al, 2012). Owing to their great performance, CNNs are now one of the preferred solutions for image recognition.…”

Section: State Of the Artmentioning

confidence: 99%

Spatio-temporal Road Detection from Aerial Imagery using CNNs

Luque¹,

Rubió²,

Ruiz-Hidalgo³

2017

Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Application

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Abstract:The main goal of this paper is to detect roads from aerial imagery recorded by drones. To achieve this, we propose a modification of SegNet, a deep fully convolutional neural network for image segmentation. In order to train this neural network, we have put together a database containing videos of roads from the point of view of a small commercial drone. Additionally, we have developed an image annotation tool based on the watershed technique, in order to perform a semi-automatic labeling of the videos in this database. The experimental results using our modified version of SegNet show a big improvement on the performance of the neural network when using aerial imagery, obtaining over 90% accuracy.

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“…In order to classify one pixel as a member of a class "road, " there are a number of possible segmentation methods based on color, texture descriptors based on statistic parameters, structure, or frequency spectrum, etc. While some acceptable results have been obtained when the color components have been used only, even three decades ago [14] or by use of the best candidates among texture statistic and structure descriptors [15], our reasoning here was oriented toward a more complex approach where the color and texture are simultaneously considered [16].…”

Section: Road Region Extractionmentioning

confidence: 99%

“…Online training upgrade of SVM method [16] is optional but is very useful in the context of this application. Besides the already mentioned updating the DTD, it includes the evaluation of the performance of current classification.…”

Section: Road Region Extractionmentioning

confidence: 99%

A classification of on-road obstacles according to their relative velocities

Bendjaballah

Graovac

Boulahlib

2016

J Image Video Proc.

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The systems based on image processing have numerous applications in the domain of motion control of robots and autonomous vehicles. The current paper is oriented to the solution of the problem that precedes the implementation of automatic avoidance of the on-road obstacles-how to detect them, to track in the sequence of images, and to recognize which of them are stationary, incoming, or outgoing from the camera. The overall algorithm of obstacle classification presented in this paper consists of three basic phases: (1) image segmentation in order to extract the pixels belonging to the image of a road and the objects over it; (2) extraction of characteristic points inside the area of the obstacle, their description and tracking in following frames; and (3) estimation of distances between the camera, the obstacles and their rates of change (relative velocities). The verifications of particular steps of the proposed algorithm are illustrated using real road-traffic images, while the overall algorithm is tested using both synthesized sequences of images and the ones acquired in real driving.

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Road detection using support vector machine based on online learning and evaluation

Cited by 88 publications

References 6 publications

A Hybrid Vision-Map Method for Urban Road Detection

A Hybrid Vision-Map Method for Urban Road Detection

Spatio-temporal Road Detection from Aerial Imagery using CNNs

A classification of on-road obstacles according to their relative velocities

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