Track Detection for Autonomous Trains

Gschwandtner, Michael; Pree, Wolfgang; Uhl, Andreas

doi:10.1007/978-3-642-17277-9_3

Cited by 18 publications

(12 citation statements)

References 9 publications

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“…At the time of writing, there are no works that address the task of anomaly detection through visual data in the railway scenario during nighttime. Only similar task have been addressed, such as track detection [12,17,36] and collision prediction [22,23]. Unfortunately, datasets are not often publicly available.…”

Section: Anomaly Detection On Railwaysmentioning

confidence: 99%

Anomaly Detection for Vision-Based Railway Inspection

Gasparini

Pini

Borghi

et al. 2020

Communications in Computer and Information Science

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The automatic inspection of railways for the detection of obstacles is a fundamental activity in order to guarantee the safety of the train transport. Therefore, in this paper, we propose a vision-based framework that is able to detect obstacles during the night, when the train circulation is usually suspended, using RGB or thermal images. Acquisition cameras and external light sources are placed in the frontal part of a rail drone and a new dataset is collected. Experiments show the accuracy of the proposed approach and its suitability, in terms of computational load, to be implemented on a self-powered drone.

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Section: Anomaly Detection On Railwaysmentioning

confidence: 99%

Anomaly Detection for Vision-Based Railway Inspection

Gasparini

Pini

Borghi

et al. 2020

Communications in Computer and Information Science

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“…This reliability value is used to determine if the detected edge is an actual road edge or from other irrelevant objects, shadow or pedestrians in front of the vehicle. After all edges on both sides are detected and verified, q-Bezier curve [6,13,14] is used to model the curve of one of the lines or road edges and perspective geometry information is used to model the other line or edge (Figure 1(d)). …”

Section: Introductionmentioning

confidence: 99%

Human-like Driving for Autonomous Vehicles using Vision-based Road Curvature Modeling

2013

IJHIT

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“…There are also systems designed to use cabin cameras for detecting obstacles in front of trains [70,71], or detecting turnouts for deciding about the path of the train [72,73].…”

Section: Vision-based Railway Detection Systemsmentioning

confidence: 99%

“…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%

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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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Track Detection for Autonomous Trains

Cited by 18 publications

References 9 publications

Anomaly Detection for Vision-Based Railway Inspection

Anomaly Detection for Vision-Based Railway Inspection

Human-like Driving for Autonomous Vehicles using Vision-based Road Curvature Modeling

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

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