The effective use of the exhaustive search block matching algorithm in railway line tracking

Chekure, Enock Tafadzwa; Naudé, Kevin A.; Freere, Peter

doi:10.1109/afrcon.2017.8095694

Cited by 3 publications

(2 citation statements)

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“…According to the dimensions of the data here and then the dimensions of the covariance matrix, three eigenvalues are calculated in each local neighborhood. If A is the covariance matrix of points in a local neighborhood, the eigenvalues (λ) of this matrix are obtained by solving Equation (4).…”

Section: Rail Line Detectionmentioning

confidence: 99%

“…As a result, many studies have been performed using these techniques in order to recognize and inspect railway tracks. In most of these studies, the recorded images or videos (using a camera mounted on a train or automatic vehicle) and their analysis are used to help the driver assistance system [ 4 , 5 , 6 , 7 ] or automatically detect obstacles along the tracks by extracting railway lines [ 8 ]. In recent years, with the advancement of technologies such as GPS, laser scanners (installed on trains, land, or air), mobile mapping systems, and drones, a wide range of rail monitoring systems have been offered to inspectors.…”

Section: Introductionmentioning

confidence: 99%

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Rail Track Detection and Projection-Based 3D Modeling from UAV Point Cloud

Sahebdivani

Arefi

Maboudi

2020

Sensors

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The expansion of the railway industry has increased the demand for the three-dimensional modeling of railway tracks. Due to the increasing development of UAV technology and its application advantages, in this research, the detection and 3D modeling of rail tracks are investigated using dense point clouds obtained from UAV images. Accordingly, a projection-based approach based on the overall direction of the rail track is proposed in order to generate a 3D model of the railway. In order to extract the railway lines, the height jump of points is evaluated in the neighborhood to select the candidate points of rail tracks. Then, using the RANSAC algorithm, line fitting on these candidate points is performed, and the final points related to the rail are identified. In the next step, the pre-specified rail piece model is fitted to the rail points through a projection-based process, and the orientation parameters of the model are determined. These parameters are later improved by fitting the Fourier curve, and finally a continuous 3D model for all of the rail tracks is created. The geometric distance of the final model from rail points is calculated in order to evaluate the modeling accuracy. Moreover, the performance of the proposed method is compared with another approach. A median distance of about 3 cm between the produced model and corresponding point cloud proves the high quality of the proposed 3D modeling algorithm in this study.

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Section: Rail Line Detectionmentioning

confidence: 99%