Rail extraction for driver support in railways

Nassu, Bogdan Tomoyuki; Ukai, Masato

doi:10.1109/ivs.2011.5940410

Cited by 28 publications

(14 citation statements)

References 7 publications

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“…The method is based on the fact that the variance of the HOG features corresponding to the image blocks that contain rail track is relatively large since such a block always contains prominent edges. Evaluation results showed that the method proposed in [ 21 ] was superior to other related methods [ 23 , 27 , 34 ]. In [ 23 ], gradient-based edge detection is used for rail tracks extraction.…”

Section: Vision-based On-board Obstacle Detection In Railwaysmentioning

confidence: 99%

“…A number of authors present the application of multi-camera systems and long-range cameras to advance environment perception performance. For example, in [ 27 ], various on-board camera models and lenses were used to extract rail tracks in short as well as in long distances. A vision-based system consisting of a high-resolution camera capable of detecting relevant objects within a distance of 500 m that is mounted on the body of the locomotive to have a clear view of the scene in front of the train is presented in [ 28 ].…”

Section: Introductionmentioning

confidence: 99%

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A Review of Vision-Based On-Board Obstacle Detection and Distance Estimation in Railways

Ristić-Durrant

Franke

Michels

2021

Sensors

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This paper provides a review of the literature on vision-based on-board obstacle detection and distance estimation in railways. Environment perception is crucial for autonomous detection of obstacles in a vehicle’s surroundings. The use of on-board sensors for road vehicles for this purpose is well established, and advances in Artificial Intelligence and sensing technologies have motivated significant research and development in obstacle detection in the automotive field. However, research and development on obstacle detection in railways has been less extensive. To the best of our knowledge, this is the first comprehensive review of on-board obstacle detection methods for railway applications. This paper reviews currently used sensors, with particular focus on vision sensors due to their dominant use in the field. It then discusses and categorizes the methods based on vision sensors into methods based on traditional Computer Vision and methods based on Artificial Intelligence.

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Section: Vision-based On-board Obstacle Detection In Railwaysmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Review of Vision-Based On-Board Obstacle Detection and Distance Estimation in Railways

Ristić-Durrant

Franke

Michels

2021

Sensors

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“…(10) Nassu and Ukai presented an approach to extract rails by matching edge features to candidate rail patterns modeled as sequences of parabola segments. (11) Ussyshkin and Smith proposed a simplified approach, that is, evaluation of the vertical and horizontal accuracies separately for a corridor mapping application. (12) Most of the research studies handled only a few wires or rails, and researchers also said that it is difficult to reconstruct whole line objects automatically in a complex area where many line-type facilities are installed.…”

Section: Introductionmentioning

confidence: 99%

Automated Reconstruction of Railroad Rail Using Helicopter-borne Light Detection and Ranging in a Train Station

Jeon¹,

Kim²

2019

Sensors and Materials

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A coordinate-based 3D model of a railroad rail is essential for the maintenance of railway services. However, automated processing to reconstruct objects from light detection and ranging (LiDAR) data in areas where such facilities are installed in a complex manner is still a challenge. In this study, our objective is to develop a method for the automated reconstruction of rails from LiDAR data in a complex area. Unlike the running sections of a train where one or two rails are present, many rails are installed by joining or branching in a railway station. Three factors, namely, height difference, spatial relationship with other objects, and point density difference, were considered in detecting the rails in this complicated area. For tracking and modeling rails, an iterative random sample consensus (RANSAC) and singular value decomposition (SVD) algorithms were used. The results showed that about 90% of the rail tracks were extracted, and the precision rate was about 99%. All processes were fully automated, and the method proposed in this study was developed to be applicable to various line-type facilities as well as rails.

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“…The localization of railways was considered in previous studies [70][71][72][74][75][76], but the problem is not considered satisfactorily solved yet. Most of the proposed methods are sensitive to environmental conditions.…”

Section: Vision-based Railway Detection Systemsmentioning

confidence: 99%

“…In [74], the extraction of the rails is done by applying template matching with pre-defined rail patterns on the near-range regions of cabin views in front of the train. For the far-range regions, rail patterns are computed at runtime according to the position of the detected rail segments in the near-range regions.…”

Section: Cabin View Based Approachesmentioning

confidence: 99%

Video Analytics for the Detection of Near-Miss Incidents at Railway Level Crossings and Signal Passed at Danger Events

Aminmansour¹

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The work contained in this thesis has not been previously submitted to meet requirements for an award at this or any other higher education institution. To the best of my knowledge and belief, the thesis contains no material previously published or written by another person except where due reference is made. Currently most of the data for near-miss and SPAD incidents are collected from the reports made by train drivers. There is a doubt that all of the incidents that occurred are actually reported by train drivers, due to the subjectivity of near-miss events, and also disciplinary actions that v might be taken against train drivers, if they are responsible for any incidents.To identify the causal factors of railway collisions, the Cooperative Research Centre for Rail Innovation has initiated a research project to develop an automated data collection system to support railway collision analysis. The system collects data from trains' on-board computers, GPS receivers, and forward facing cameras. This thesis presents computer vision algorithms for automatically detecting near-miss and SPAD incidents from train cameras as part of this project.To automatically detect near-miss events from videos, we localize vehicles in each frame and calculate the distance of the vehicles to the railway. If the distance of a vehicle to the railway is less than a pre-defined distance, the event will be flagged as a near-miss incident for further analysis. Our proposed near-miss detection system consists of railway detection and segmentation, and vehicle detection and localization algorithms. We employ several computer vision techniques including image segmentation, convolutional neural networks, and 3D geometry reconstruction.In order to automatically detect SPAD incidents from videos, we detect railway signals as well as the state of the signals in each frame. An event will be flagged as a SPAD if a train passes a red signal. Our approach for detecting SPAD events consists of using morphological operations, different colour spaces, 3D geometry reconstruction, segmentation algorithms, and machine learning techniques.This thesis introduces algorithms for automatically detecting level crossing near-miss and SPAD incidents from video data obtained by forward facing cameras of trains. The accuracy rate of our near-miss detection system for detecting and segmenting railways on our railway dataset is 96% for day-time, and 93% for night-time videos. The system has 75% of precision and 81% of recall for detecting vehicles on this dataset. We compared our vehicle detector algorithm with the Deformable Part Models (DPM) and Regions with CNN features (R-CNN) object detectors. The recall rate of our algorithm is 29% higher than what can be achieved with DPM or R-CNN detectors on our railway dataset. We also present the results of detecting railway signals on more than 5,000 frames for our SPAD detection system. This thesis opens avenues for helping to identify the causal factors of train collisions.vi

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Rail extraction for driver support in railways

Cited by 28 publications

References 7 publications

A Review of Vision-Based On-Board Obstacle Detection and Distance Estimation in Railways

A Review of Vision-Based On-Board Obstacle Detection and Distance Estimation in Railways

Automated Reconstruction of Railroad Rail Using Helicopter-borne Light Detection and Ranging in a Train Station

Video Analytics for the Detection of Near-Miss Incidents at Railway Level Crossings and Signal Passed at Danger Events

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