Table of Contents Author Guidelines Submit a Manuscript
Journal of Sensors
Volume 2016, Article ID 8410731, 13 pages
http://dx.doi.org/10.1155/2016/8410731
Research Article

Feature Selection for Intelligent Firefighting Robot Classification of Fire, Smoke, and Thermal Reflections Using Thermal Infrared Images

1Mechanical & Systems Engineering Department, Korea Military Academy, Seoul, Republic of Korea
2Mechanical Engineering Department, Virginia Tech, Blacksburg, VA 24060, USA

Received 26 March 2016; Revised 5 July 2016; Accepted 3 August 2016

Academic Editor: Juan A. Corrales

Copyright © 2016 Jong-Hwan Kim et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. J.-H. Kim and B. Y. Lattimer, “Real-time probabilistic classification of fire and smoke using thermal imagery for intelligent firefighting robot,” Fire Safety Journal, vol. 72, pp. 40–49, 2015. View at Publisher · View at Google Scholar · View at Scopus
  2. J. W. Starr and B. Y. Lattimer, “Evaluation of navigation sensors in fire smoke environments,” Fire Technology, vol. 50, no. 6, pp. 1459–1481, 2014. View at Publisher · View at Google Scholar · View at Scopus
  3. J.-H. Kim, B. Keller, and B. Y. Lattimer, “Sensor fusion based seek-and-find fire algorithm for intelligent firefighting robot,” in Proceedings of the IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM '13), pp. 1482–1486, IEEE, Wollongong, Australia, July 2013. View at Publisher · View at Google Scholar · View at Scopus
  4. J. G. McNeil, J. Starr, and B. Y. Lattimer, “Autonomous fire suppression using multispectral sensors,” in Proceedings of the IEEE/ASME International Conference on Advanced Intelligent Mechatronics: Mechatronics for Human Wellbeing (AIM '13), pp. 1504–1509, Wollongong, Austrailia, July 2013. View at Publisher · View at Google Scholar · View at Scopus
  5. J.-H. Kim, J. W. Starr, and B. Y. Lattimer, “Firefighting robot stereo infrared vision and radar sensor fusion for imaging through smoke,” Fire Technology, vol. 51, no. 4, pp. 823–845, 2015. View at Publisher · View at Google Scholar · View at Scopus
  6. R. C. Luo and K. L. Su, “Autonomous fire-detection system using adaptive sensory fusion for intelligent security robot,” IEEE/ASME Transactions on Mechatronics, vol. 12, no. 3, pp. 274–281, 2007. View at Publisher · View at Google Scholar · View at Scopus
  7. M. A. Jackson and I. Robins, “Gas sensing for fire detection: measurements of CO, CO2, H2, O2, and smoke density in European standard fire tests,” Fire Safety Journal, vol. 22, no. 2, pp. 181–205, 1994. View at Publisher · View at Google Scholar · View at Scopus
  8. B. U. Töreyin, R. G. Cinbiş, Y. Dedeoğlu, and A. E. Çetin, “Fire detection in infrared video using wavelet analysis,” Optical Engineering, vol. 46, no. 6, Article ID 067204, 2007. View at Publisher · View at Google Scholar · View at Scopus
  9. B. U. Toreyin, Y. Dedeoglu, and A. E. Cetin, “Wavelet based real-time smoke detection in video,” in Proceedings of the 13th European Signal Processing Conference, pp. 4–8, Antalya, Turkey, September 2005.
  10. T. Celik, H. Demirel, H. Ozkaramanli, and M. Uyguroglu, “Fire detection using statistical color model in video sequences,” Journal of Visual Communication and Image Representation, vol. 18, no. 2, pp. 176–185, 2007. View at Publisher · View at Google Scholar · View at Scopus
  11. L. Merino, F. Caballero, J. R. Martínez-de-Dios, I. Maza, and A. Ollero, “An unmanned aircraft system for automatic forest fire monitoring and measurement,” Journal of Intelligent & Robotic Systems, vol. 65, no. 1, pp. 533–548, 2012. View at Publisher · View at Google Scholar · View at Scopus
  12. Y. Wang, T. W. Chua, R. Chang, and N. T. Pham, “Real-time smoke detection using texture and color features,” in Proceedings of the 21st International Conference on Pattern Recognition (ICPR '12), pp. 1727–1730, Tsukuba, Japan, November 2012. View at Scopus
  13. G. Marbach, M. Loepfe, and T. Brupbacher, “An image processing technique for fire detection in video images,” Fire Safety Journal, vol. 41, no. 4, pp. 285–289, 2006. View at Publisher · View at Google Scholar · View at Scopus
  14. M. I. Chacon-Murguia and F. J. Perez-Vargas, “Thermal video analysis for fire detection using shape regularity and intensity saturation features,” in Pattern Recognition, J. F. Martínez-Trinidad, J. A. Carrasco-Ochoa, C. B.-Y. Brants, and E. R. Hancock, Eds., vol. 6718 of Lecture Notes in Computer Science, pp. 118–126, Springer, Berlin, Germany, 2011. View at Publisher · View at Google Scholar
  15. W. Phillips III, M. Shah, and N. D. V. Lobo, “Flame recognition in video,” Pattern Recognition Letters, vol. 23, no. 1–3, pp. 319–327, 2002. View at Publisher · View at Google Scholar · View at Scopus
  16. D. Han and B. Lee, “Development of early tunnel fire detection algorithm using the image processing,” in Advances in Visual Computing, pp. 39–48, Springer, Berlin, Germany, 2006. View at Google Scholar
  17. Y. Chunyu, F. Jun, W. Jinjun, and Z. Yongming, “Video fire smoke detection using motion and color features,” Fire Technology, vol. 46, no. 3, pp. 651–663, 2010. View at Publisher · View at Google Scholar · View at Scopus
  18. F. Yuan, “Video-based smoke detection with histogram sequence of LBP and LBPV pyramids,” Fire Safety Journal, vol. 46, no. 3, pp. 132–139, 2011. View at Publisher · View at Google Scholar · View at Scopus
  19. F. Lafarge, X. Descombes, and J. Zerubia, “Textural kernel for SVM classification in remote sensing: application to forest fire detection and Urban area extraction,” in Proceedings of the IEEE International Conference on Image Processing (ICIP '05), pp. 1096–1099, September 2005. View at Publisher · View at Google Scholar · View at Scopus
  20. F. Amon and A. Ducharme, “Image frequency analysis for testing of fire service thermal imaging cameras,” Fire Technology, vol. 45, no. 3, pp. 313–322, 2009. View at Publisher · View at Google Scholar · View at Scopus
  21. F. Amon, V. Benetis, J. Kim, and A. Hamins, “Development of a performance evaluation facility for fire fighting thermal imagers,” in Defense and Security, pp. 244–252, 2004. View at Google Scholar
  22. F. D. Maxwell, “A portable IR system for observing fire thru smoke,” Fire Technology, vol. 7, no. 4, pp. 321–331, 1971. View at Publisher · View at Google Scholar · View at Scopus
  23. A. Barducci, D. Guzzi, P. Marcoionni, and I. Pippi, “Infrared detection of active fires and burnt areas: theory and observations,” Infrared Physics & Technology, vol. 43, no. 3–5, pp. 119–125, 2002. View at Publisher · View at Google Scholar · View at Scopus
  24. C. Wang and S. Qin, “Adaptive detection method of infrared small target based on target-background separation via robust principal component analysis,” Infrared Physics & Technology, vol. 69, pp. 123–135, 2015. View at Publisher · View at Google Scholar · View at Scopus
  25. N. Aggarwal and R. K. Agrawal, “First and second order statistics features for classification of magnetic resonance brain images,” Journal of Signal and Information Processing, vol. 3, no. 2, pp. 146–153, 2012. View at Publisher · View at Google Scholar
  26. B. Ko, K.-H. Cheong, and J.-Y. Nam, “Early fire detection algorithm based on irregular patterns of flames and hierarchical Bayesian Networks,” Fire Safety Journal, vol. 45, no. 4, pp. 262–270, 2010. View at Publisher · View at Google Scholar · View at Scopus
  27. H. Maruta, Y. Kato, A. Nakamura, and F. Kurokawa, “Smoke detection in open areas using its texture features and time series properties,” in Proceedings of the IEEE International Symposium on Industrial Electronics (ISIE '09), pp. 1904–1908, IEEE, Seoul, South Korea, July 2009. View at Publisher · View at Google Scholar · View at Scopus
  28. C. M. Bautista, C. A. Dy, M. I. Mañalac, R. A. Orbe, and M. Cordel, “Convolutional neural network for vehicle detection in low resolution traffic videos,” in Proceedings of the IEEE Region 10 Symposium (TENSYMP ), pp. 277–281, IEEE, Bali, Indonesia, May 2016. View at Publisher · View at Google Scholar
  29. H. Wang, Y. Cai, X. Chen, and L. Chen, “Night-time vehicle sensing in far infrared image with deep learning,” Journal of Sensors, vol. 2016, Article ID 3403451, 8 pages, 2016. View at Publisher · View at Google Scholar · View at Scopus
  30. C. Shen, Z. Bai, H. Cao et al., “Optical flow sensor/INS/magnetometer integrated navigation system for MAV in GPS-denied environment,” Journal of Sensors, vol. 2016, Article ID 6105803, 10 pages, 2016. View at Publisher · View at Google Scholar
  31. A. Bruhn, J. Weickert, and C. Schnörr, “Lucas/Kanade meets Horn/Schunck: combining local and global optic flow methods,” International Journal of Computer Vision, vol. 61, no. 3, pp. 1–21, 2005. View at Publisher · View at Google Scholar · View at Scopus
  32. A. S. N. Huda and S. Taib, “Suitable features selection for monitoring thermal condition of electrical equipment using infrared thermography,” Infrared Physics and Technology, vol. 61, pp. 184–191, 2013. View at Publisher · View at Google Scholar · View at Scopus
  33. R. M. Haralick, K. Shanmugam, and I. H. Dinstein, “Textural features for image classification,” IEEE Transactions on Systems, Man and Cybernetics, vol. 3, no. 6, pp. 610–621, 1973. View at Publisher · View at Google Scholar · View at Scopus
  34. N. Otsu, “A threshold selection method from gray-level histograms,” Automatica, vol. 11, pp. 23–27, 1975. View at Google Scholar
  35. P. Harrington, Machine Learning in Action, Manning Publications, 2012.
  36. D. J. Hand, H. Mannila, and P. Smyth, Principles of Data Mining, MIT Press, 2001.
  37. D. Lin, X. Xu, and F. Pu, “Bayesian information criterion based feature filtering for the fusion of multiple features in high-spatial-resolution satellite scene classification,” Journal of Sensors, vol. 2015, Article ID 142612, 10 pages, 2015. View at Publisher · View at Google Scholar · View at Scopus
  38. F. Van Der Heijden, R. Duin, D. De Ridder, and D. M. Tax, Classification, Parameter Estimation and State Estimation: An Engineering Approach Using MATLAB, John Wiley & Sons, 2005.
  39. R. Kohavi, “A study of cross-validation and bootstrap for accuracy estimation and model selection,” in Proceedings of the 14th International Joint Conference on Artificial Intelligence (IJCAI '95), Montreal, Canada, August 1995.
  40. K. Deb, Multi-Objective Optimization Using Evolutionary Algorithms, vol. 16, John Wiley & Sons, New York, NY, USA, 2001.