Large collections of GPS trajectory data provide us unprecedented opportunity to detect the roadintersection automatically. However, in the real-world scenarios, the precision of existing detection methods cannot be ...
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ISBN:
(纸本)9781728133638
Large collections of GPS trajectory data provide us unprecedented opportunity to detect the roadintersection automatically. However, in the real-world scenarios, the precision of existing detection methods cannot be guaranteed due to severe challenges including (i) low-quality raw GPS trajectory data and (ii) the difficulty of differentiating intersections from non-intersections. To tackle above issues, we propose a novel twophase road intersection detection framework, called as RIDF, which is comprised of trajectory quality improving and intersection extracting. More importantly, through extracting candidate cells based on direction statistic analysis and refining the locations of intersections using hybrid clustering strategy, our approach can effectively detect roadintersections of different size. An experimental evaluation on two real data sets extensively assesses the quality of RIDF method by comparing it with state-of-the-art methods. Experimental results demonstrate that our proposal can overcome the limitations of existing methods and thus have better accuracy than the existing work.
road is the skeleton of the city, which is usually elongated. Extracting road information is of great significance in urban planning. Tensor voting can detect the geometric characteristics of the typical objects in th...
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ISBN:
(纸本)9781509033324
road is the skeleton of the city, which is usually elongated. Extracting road information is of great significance in urban planning. Tensor voting can detect the geometric characteristics of the typical objects in the image. In this paper in order to improve the accuracy of road extraction in high-resolution remote sensing image, road is extracted preliminary using morphological edge segmentation and shape index feature extraction, then tensor voting is introduced to purify the road information and detect roadintersections. Voting in stick field can detect and amplify the stick saliency of road pixels, the gaps can also be connected after tensor voting. On the basis of pure road extraction result above, the voting in ball field is used to detect the roadintersections. The experiments on remote sensing images show that the proposed method in this paper is superior to traditional morphological post-processing method, it can precisely extract the roads, and detect all the roadintersections at the same time.
In this paper, a hierarchical multi-classification approach using support vector machines (SVM) has been proposed for road intersection detection and classification. Our method has two main steps. The first involves t...
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In this paper, a hierarchical multi-classification approach using support vector machines (SVM) has been proposed for road intersection detection and classification. Our method has two main steps. The first involves the roaddetection. For this purpose, an edge-based approach has been developed using the bird's eye view image which is mapped from the perspective view of the road scene. Then, the concept of vertical spoke has been introduced for road boundary form extraction. The second step deals with the problem of road intersection detection and classification. It consists on building a hierarchical SVM classifier of the extracted road forms using the unbalanced decision tree architecture. Many measures are incorporated for good evaluation of the proposed solution. The obtained results are compared to those of Choi et al. (2007).
road separations, intersections, and crosswalks, which are important components of highways, are seen as significant areas for autonomous vehicles and advanced driver assistance systems because traffic accident occurr...
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road separations, intersections, and crosswalks, which are important components of highways, are seen as significant areas for autonomous vehicles and advanced driver assistance systems because traffic accident occurrence rate is considerably high in these areas. In this study, an image processing method and a deep learning based approach on real images has been proposed in order to provide instant information for drivers and autonomous vehicles, or to develop warning systems as part of advanced driver assistance systems to prevent or minimize traffic accidents. The information is obtained from the classification of images belonging to the separations, intersections and crosswalks on the road using a new model and VggNet, AlexNet, LeNet based on Convolutional Neural Network(CNN). We have obtained high classification accuracy with our model based on CNN. The result of the study performed on different datasets showed that the proposed method is usable for driver assistance systems and an effective structure that can be used in many areas such as warning both vehicles and drivers. (C) 2019 Elsevier B.V. All rights reserved.
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