Train Detection and Tracking in Optical Time Domain Reflectometry (OTDR) Signals

  • Adam PappEmail author
  • Christoph Wiesmeyr
  • Martin Litzenberger
  • Heinrich Garn
  • Walter Kropatsch
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9796)


We propose a novel method for the detection of vibrations caused by trains in an optical fiber buried nearby the railway track. Using optical time-domain reflectometry vibrations in the ground caused by different sources can be detected with high accuracy in time and space. While several algorithms have been proposed in the literature for train tracking using OTDR signals they have not been tested on longer recordings. The presented method learns the characteristic pattern in the Fourier domain using a support vector machine (SVM) and it becomes more robust to any kind of noise and artifacts in the signal. The point-based causal train tracking has two stages to minimize the influence of false classifications of the vibration detection. Our technical contribution is the evaluation of the presented algorithm based on two hour long recording and demonstration of open problems for commercial usage.


Support Vector Machine Feature Vector Gaussian Mixture Model Intrusion Detection Tracking Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The data used for the work presented in this paper have been collected in the research project Sensorsystem für Bahnstrecken funded by the Austrian Research Agency FFG under contract number 840448. We especially acknowledge Wolfgang Zottl (ÖBB Infrastruktur GmbH) for approving the measurements and Günther Neunteufel (Fiber Cable Technologies GmbH) for providing the data.


  1. 1.
    Bao, X., Chen, L.: Recent progress in distributed fiber optic sensors. Sensors 12(7), 8601–8639 (2012)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Chen, C., Chen, R., Wei, F., Wu, D.H.: Experimental and application of spiral distributed optical fiber sensors based on OTDR. In: 2011 International Conference on Electric Information and Control Engineering (ICEICE), pp. 5905–5909. IEEE (2011)Google Scholar
  3. 3.
    Choi, K.N., Juarez, J.C., Taylor, H.F.: Distributed fiber optic pressure/seismic sensor for low-cost monitoring of long perimeters. In: AeroSense 2003. International Society for Optics and Photonics, pp. 134–141 (2003)Google Scholar
  4. 4.
    Comaniciu, D., Ramesh, V., Meer, P.: Kernel-based object tracking. IEEE Trans. Pattern Anal. Mach. Intell. 25(5), 564–577 (2003)CrossRefGoogle Scholar
  5. 5.
    Jiang, H., Fels, S., Little, J.J.: A linear programming approach for multiple object tracking. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2007, pp. 1–8. IEEE (2007)Google Scholar
  6. 6.
    Juarez, J.C., Maier, E.W., Choi, K.N., Taylor, H.F.: Distributed fiber-optic intrusion sensor system. J. Lightwave Technol. 23(6), 2081 (2005)CrossRefGoogle Scholar
  7. 7.
    Kalman, R.E.: A new approach to linear filtering and prediction problems. J. Basic Eng. 82(1), 35–45 (1960)CrossRefGoogle Scholar
  8. 8.
    Kanellopoulos, S., Shatalin, S.: Detecting a disturbance in the phase of light propagating in an optical waveguide, 11 September 2012, US Patent 8,264,676.
  9. 9.
    Kong, H., Zhou, Q., Xie, W., Dong, Y., Ma, C., Hu, W.: Events detection in OTDR data based on a method combining correlation matching with STFT. In: Asia Communications and Photonics Conference, pp. ATh3A–148. Optical Society of America (2014)Google Scholar
  10. 10.
    Kumagai, T., Sato, S., Nakamura, T.: Fiber-optic vibration sensor for physical security system. In: 2012 International Conference on Condition Monitoring and Diagnosis (CMD), pp. 1171–1174. IEEE (2012)Google Scholar
  11. 11.
    Papp, A., Wiesmeyr, C., Litzenberger, M., Garn, H., Kropatsch, W.: A real-time algorithm for train position monitoring using optical time-domain reflectometry. In: IEEE International Conference on Intelligent Rail Transportation (accepted) (2016)Google Scholar
  12. 12.
    Peng, F., Duan, N., Rao, Y.J., Li, J.: Real-time position and speed monitoring of trains using phase-sensitive OTDR. IEEE Photonics Technol. Lett. 26(20), 2055–2057 (2014)CrossRefGoogle Scholar
  13. 13.
    Peng, F., Wu, H., Jia, X.H., Rao, Y.J., Wang, Z.N., Peng, Z.P.: Ultra-long high-sensitivity \(\phi \)-OTDR for high spatial resolution intrusion detection of pipelines. Opt. Express 22(11), 13804–13810 (2014)CrossRefGoogle Scholar
  14. 14.
    Qin, Z., Chen, L., Bao, X.: Wavelet denoising method for improving detection performance of distributed vibration sensor. IEEE Photonics Technol. Lett. 24(7), 542–544 (2012)CrossRefGoogle Scholar
  15. 15.
    Rangarajan, K., Shah, M.: Establishing motion correspondence. In: 1991 Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 1991, pp. 103–108. IEEE (1991)Google Scholar
  16. 16.
    Sethi, I.K., Jain, R.: Finding trajectories of feature points in a monocular image sequence. IEEE Trans. Pattern Anal. Mach. Intell. 1, 56–73 (1987)CrossRefGoogle Scholar
  17. 17.
    Shafique, K., Shah, M.: A noniterative greedy algorithm for multiframe point correspondence. IEEE Trans. Pattern Anal. Mach. Intell. 27(1), 51–65 (2005)CrossRefGoogle Scholar
  18. 18.
    Shi, J., Tomasi, C.: Good features to track. In: 1994 Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 1994, pp. 593–600. IEEE (1994)Google Scholar
  19. 19.
    Timofeev, A.V.: Monitoring the railways by means of C-OTDR technology. Int. J. Mech. Aerosp. Ind. Mechatron. Eng. 9(5), 701–704 (2015)Google Scholar
  20. 20.
    Timofeev, A.V., Egorov, D.V., Denisov, V.M.: The rail traffic management with usage of C-OTDR monitoring systems. World Acad. Sci. Eng. Technol. Int. J. Comput. Electr. Autom. Control Inf. Eng. 9(7), 1492–1495 (2015)Google Scholar
  21. 21.
    Timofeev, A., Egorov, D.: Multichannel classification of target signals by means of an SVM ensemble in C-OTDR systems for remote monitoring of extended objects. In: MVML-2014 Conference Proceedings, vol. 1 (2014)Google Scholar
  22. 22.
    Wu, H., Li, X., Peng, Z., Rao, Y.: A novel intrusion signal processing method for phase-sensitive optical time-domain reflectometry (\(\phi \)-OTDR). In: OFS2014 23rd International Conference on Optical Fiber Sensors. p. 91575O. International Society for Optics and Photonics (2014)Google Scholar
  23. 23.
    Yilmaz, A., Javed, O., Shah, M.: Object tracking: a survey. ACM Comput. Surv. (CSUR) 38(4), 13 (2006)CrossRefGoogle Scholar
  24. 24.
    You, C.H., Lee, K.A., Li, H.: GMM-SVM kernel with a Bhattacharyya-based distance for speaker recognition. IEEE Trans. Audio Speech Lang. Process. 18(6), 1300–1312 (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Adam Papp
    • 1
    Email author
  • Christoph Wiesmeyr
    • 1
  • Martin Litzenberger
    • 1
  • Heinrich Garn
    • 1
  • Walter Kropatsch
    • 2
  1. 1.Digital Safety and Security DepartmentAustrian Institute of Technology GmbHViennaAustria
  2. 2.Pattern Recognition and Image Processing GroupVienna University of TechnologyViennaAustria

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