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

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Pattern Recognition (GCPR 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9796))

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Abstract

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.

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References

  1. Bao, X., Chen, L.: Recent progress in distributed fiber optic sensors. Sensors 12(7), 8601–8639 (2012)

    Article  MathSciNet  Google Scholar 

  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. 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. Comaniciu, D., Ramesh, V., Meer, P.: Kernel-based object tracking. IEEE Trans. Pattern Anal. Mach. Intell. 25(5), 564–577 (2003)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  7. Kalman, R.E.: A new approach to linear filtering and prediction problems. J. Basic Eng. 82(1), 35–45 (1960)

    Article  Google Scholar 

  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. https://www.google.com/patents/US8264676

  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. 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. 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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  17. Shafique, K., Shah, M.: A noniterative greedy algorithm for multiframe point correspondence. IEEE Trans. Pattern Anal. Mach. Intell. 27(1), 51–65 (2005)

    Article  Google Scholar 

  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. 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. 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. 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. 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. Yilmaz, A., Javed, O., Shah, M.: Object tracking: a survey. ACM Comput. Surv. (CSUR) 38(4), 13 (2006)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

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Acknowledgements

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.

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Authors and Affiliations

  1. Digital Safety and Security Department, Austrian Institute of Technology GmbH, Vienna, Austria

    Adam Papp, Christoph Wiesmeyr, Martin Litzenberger & Heinrich Garn

  2. Pattern Recognition and Image Processing Group, Vienna University of Technology, Vienna, Austria

    Walter Kropatsch

Authors
  1. Adam Papp
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  2. Christoph Wiesmeyr
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  3. Martin Litzenberger
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  4. Heinrich Garn
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  5. Walter Kropatsch
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Corresponding author

Correspondence to Adam Papp .

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Editors and Affiliations

  1. University of Hannover, Hannover, Germany

    Bodo Rosenhahn

  2. Max Planck Institute for Informatics, Saarbrücken, Germany

    Bjoern Andres

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Papp, A., Wiesmeyr, C., Litzenberger, M., Garn, H., Kropatsch, W. (2016). Train Detection and Tracking in Optical Time Domain Reflectometry (OTDR) Signals. In: Rosenhahn, B., Andres, B. (eds) Pattern Recognition. GCPR 2016. Lecture Notes in Computer Science(), vol 9796. Springer, Cham. https://doi.org/10.1007/978-3-319-45886-1_26

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  • DOI: https://doi.org/10.1007/978-3-319-45886-1_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-45885-4

  • Online ISBN: 978-3-319-45886-1

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