MCMC PARTICLE FILTER WITH OVERRELAXATED SLICE SAMPLING FOR ACCURATE RAIL INSPECTION

Marcos Nieto, Andoni Cortés, Oihana Otaegui, Iñigo Etxabe

2012

Abstract

This paper introduces a rail inspection system which detects rail flaws using computer vision algorithms. Unlike other methods designed for the same purpose, we propose a method that automatically fits a 3D rail model to the observations during regular services and normal traffic conditions. The proposed strategy is based on a novel application of the slice sampling technique with overrelaxation in the framework of MCMC (Markov Chain Monte Carlo) particle filters. This combination allows us to efficiently exploit the temporal coherence of observations and to obtain more accurate estimates than with other techniques such as importance sampling or Metropolis-Hastings. The results show that the system is able to efficient and robustly obtain measurements of the wear of the rails, while we show as well that it is possible to introduce the slice sampling technique into MCMC particle filters.

References

  1. Alippi, C., Casagrande, E., Fumagalli, M., Scotti, F., Piuri, V., and Valsecchi, L. (2002). An embedded system methodology for real-time analysis of railways track profile. In IEEE Technology Conference on Instrumentation and Measurement, pages 747-751.
  2. Alippi, C., Casagrande, E., Scotti, F., and Piuri, V. (2000). Composite real-time image processing for railways track profile measurement. IEEE Transactions on Instrumentation and Measurement, 49(3):559-564.
  3. Arulampalam, M. S., Maskell, S., Gordon, N., and Clapp, T. (2002). A tutorial on particle filters for online nonlinear/non-gaussian bayesian tracking. IEEE Transactions on Signal Processing, 50(2):174-188.
  4. Barder, F. and Chateau, T. (2008). MCMC particle filter for real-time visual tracking of vehicles. In IEEE International Conference on Intelligent Transportation Systems, pages 539-544.
  5. Bishop, C. M. (2006). Pattern Recognition and Machine Learning (Information Science and Statistics). Springer.
  6. Cannon, D., Edel, K.-O., Grassie, S., and Sawley, K. (2003). Rail defects: an overview. Fatigue & Fracture of Engineering Materials & Structures, 26(10):865- 886.
  7. DeMenthon, D. and Davis, L. S. (1995). Model-based object pose in 25 lines of code. International Journal of Computer Vision, pages 123-141.
  8. di Scalea, F. L., Rizzo, P., Coccia, S., Bartoli, I., Fateh, M., Viola, E., and Pascale, G. (2005). Non-contact ultrasonic inspection of rails and signal processing for automatic defect detection and classification. Insight - Non-Destructive Testing and Condition Monitoring, 47(6):346-353.
  9. Gilks, W., Richardson, S., and Spiegelhalter, D. (1996). Markov Chain Monte Carlo Methods in Practice. Chapman and Hall/CRC.
  10. Gonzalez, R. and Woods, R. (2002). Digital Image Processing. Prentice Hall.
  11. Khan, Z., Balch, T., and Dellaert, F. (2005). MCMC-based particle filtering for tracking a variable number of interacting targets. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(11):1805-1819.
  12. Neal, R. M. (1998). Suppressing random walks in markov chain monte carlo using ordered overrelaxation. Learning in Graphical Models, pages 205-228.
  13. Neal, R. M. (2003). Slice sampling. Annals of Statistics, 31:705-767.
  14. Nocedal, J. and Wright, S. J. (2006). Numerical Optimization. Springer.
Download


Paper Citation


in Harvard Style

Nieto M., Cortés A., Otaegui O. and Etxabe I. (2012). MCMC PARTICLE FILTER WITH OVERRELAXATED SLICE SAMPLING FOR ACCURATE RAIL INSPECTION . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2012) ISBN 978-989-8565-04-4, pages 164-172. DOI: 10.5220/0003853901640172


in Bibtex Style

@conference{visapp12,
author={Marcos Nieto and Andoni Cortés and Oihana Otaegui and Iñigo Etxabe},
title={MCMC PARTICLE FILTER WITH OVERRELAXATED SLICE SAMPLING FOR ACCURATE RAIL INSPECTION},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2012)},
year={2012},
pages={164-172},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003853901640172},
isbn={978-989-8565-04-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2012)
TI - MCMC PARTICLE FILTER WITH OVERRELAXATED SLICE SAMPLING FOR ACCURATE RAIL INSPECTION
SN - 978-989-8565-04-4
AU - Nieto M.
AU - Cortés A.
AU - Otaegui O.
AU - Etxabe I.
PY - 2012
SP - 164
EP - 172
DO - 10.5220/0003853901640172