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Introduction:
Today more than ever, reliability, convenience and speed are important factors for the commercial success of modern railways. To meet these requirements more and more automated railway monitoring systems have been deployed in the recent years. Among other things such systems visually capture entire freight trains while passing a terminal in order to identify the single railroad cars. Up to now within Europe this identi cation has to rely on visual information since the railroad car number according to the RIV (Regolamento Internazionale dei Veicoli) conventions is the only way to identify railroad cars operated by ‘foreign’ carriers.
In order to capture the visual information of the freight trains, a setup consisting of a line scan camera, arti cial lighting and inductive sensors was installed along the train line. As a preprocessing step for the vision-based identi fication it is necessary to divide the recorded continuous train image into the single railroad cars. So far the separation of the railroad cars is done using inductive sensors embedded in the railroad tracks which determine the axle patterns of the traversing railroad cars. These axle patterns are then queried and assigned to a specific car type which enables the system to estimate the bounds of a single railroad car. Since the implementation of inductive sensors is rather expensive and the axle pattern estimation of the bounds of the railroad cars is sometimes imprecise a solution based only on the captured visual information was needed and found!
The prototype which evolved from this project performed exceptionally well in the Railroad car separation from continuous train images.

Example: (Please click on the images…)
Original image taken from the “02_Graz_Tag” dataset.

original image

Result image containing the automatically detected separation points (dark blue) as well as the retrieved 1D signal (bottom).

original image


Results:
A prototype written in Matlab as well as a paper which serves as documentation.


Paper:
This paper contains an in-depth description of the algorithm as well as a detailled discussion of the evaluation results.
Robert_Hoedl_2010_HP-Railroad_Car_Separation.pdf   [.PDF | 960 K]


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© 2014 Robert Hödl Impressum