Railway track following with the AR.Drone using vanishing point detection

Páll, Előd; Mathe, Koppany; Tamás, Levente; Buşoniu, Lucian

doi:10.1109/aqtr.2014.6857870

Cited by 22 publications

(6 citation statements)

References 15 publications

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“…• Drones have also recently gained popularity for track inspections. Images are usually processed from the front camera of the drones (Pall et al, 2014). It is expected that fast image processing and analysis will be emerging in drone-based track inspection.…”

Section: Railwaysmentioning

confidence: 99%

Data-driven Transport Infrastructure Maintenance

2021

International Transport Forum Policy Papers

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Section: Railwaysmentioning

confidence: 99%

Data-driven Transport Infrastructure Maintenance

2021

International Transport Forum Policy Papers

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“…This drone development system is comprised of a drone system and ground equipment and can be used to obtain information about traffic in multiple lanes. The drone for obtaining information about traffic in multiple lanes include a multi-lane LiDAR distance detection system [33][34][35][36] based on a LiDAR distance measurement sensor. The drone system consists of an airframe, an autonomous flight control system, a LiDAR sensor, a gimbal device, a communication and video device, and a video camera that can transmit video, vehicle speed, and traffic data.…”

Section: Developed Drone System Componentmentioning

confidence: 99%

Improvement in Target Range Estimation and the Range Resolution Using Drone

Lee

2020

Electronics

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This study measured the speed of a moving vehicle in multiple lanes using a drone. The existing methods for measuring a vehicle’s speed while driving on the road measure the speed of moving automobiles by means of a sensor that is mounted on a structure. In another method, a person measures the speed of a vehicle at the edge of a road using a speed-measuring tool. The existing method for measuring a vehicle’s speed requires the installation of a gentry-structure; however, this produces a high risk for traffic accidents, which makes it impossible to measure a vehicle’s speed in multiple lanes at once. In this paper, a method that used a drone to measure the speed of moving vehicles in multiple lanes was proposed. The suggested method consisted of two LiDAR sets mounted on the drone, with each LiDAR sensor set measuring the speed of vehicles moving in one lane; that is, estimating the speed of moving vehicles in multiple lanes was possible by moving the drone over the road. The proposed method’s performance was compared with that of existing equipment in order to measure the speed of moving vehicles using the manufactured drone. The results of the experiment, in which the speed of moving vehicles was measured, showed that the Root Mean Square Error (RMSE) of the first lane and the second lane was 3.30 km/h and 2.27 km/h, respectively. The vehicle detection rate was 100% in the first lane. In the second lane, the vehicle detection rate was 94.12%, but the vehicle was not detected twice in the experiment. The average vehicle detection rate is 97.06%. Compared with the existing measurement system, the multi-lane moving vehicle speed measurement method that used the drone developed in this study reduced the risk of accidents, increased the convenience of movement, and measured the speed of vehicles moving in multiple lanes using a drone. In addition, it was more efficient than current measurement systems because it allowed an accurate measurement of speed in bad environmental conditions.

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“…In most cases valuable information in road traffic accidents exists on the road surface and only lane marks are obvious parallel lines on the ground. With this property of vanishing point, reference lines which do not exist on the ground can be marked in the form of a grid to establish the coordinate system of the road surface, thus help locating the target objects accurately in the accident [5,6]. This method is applied to an accident with surveillance video to analyze the locational relationship of the vehicles on the road ground in this article.…”

Section: Vanishing Pointmentioning

confidence: 99%

Study on Vehicle Location in Road Traffic Accident Based on Surveillance Video and Vanishing Point

Duan¹,

Liu²,

Jia³

et al. 2018

Proceedings of the 2018 International Conference on Mechanical, Electronic, Control and Automation Engineering (MECAE 2018)

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To solve the problem that it is difficult to obtain the accurate locations of target objects directly by the surveillance video in Road Traffic Accidents, this paper presents a method of measuring the relative position of vehicles on the road surface by using vanishing point principle to mesh the surveillance video frames. In a road traffic accident with surveillance video, this method was applied to analyze the dynamic positional relationship between vehicles and marking lines on the road surface. The accurate moving tracks of 2 vehicles involved were obtained and the measurement accuracy was verified. The result shows that the measurement errors are less than 3%, proving that this method has a guiding effect on obtaining the accurate locations of vehicles in Road Traffic Accidents. It can be applied to traffic accidents with surveillance video to determine the accurate locations of target objects in the process, thus providing necessary data for subsequent accident reconstruction and injury reconstruction.

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Railway track following with the AR.Drone using vanishing point detection

Cited by 22 publications

References 15 publications

Data-driven Transport Infrastructure Maintenance

Data-driven Transport Infrastructure Maintenance

Improvement in Target Range Estimation and the Range Resolution Using Drone

Study on Vehicle Location in Road Traffic Accident Based on Surveillance Video and Vanishing Point

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