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Flame detection algorithm based on a saliency detection technique and the uniform local binary pattern in the YCbCr color space

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Abstract

Computer vision-based fire detection involves flame detection and smoke detection. This paper proposes a new flame detection algorithm that is based on a saliency detection technique and on the uniform local binary pattern (ULBP). In still images and video sequences, an area that contains an open flame is always noticeable because fire is an exceptional event. Thus, to utilize the color information of flame pixels, the probability density function (pdf) of the flame pixel color can be obtained using Parzen window nonparametric estimation. This a priori pdf is then fused with the saliency detection phase as top-down information so that the flame candidate area can be extracted. To reduce the number of false alarms, the image texture of the candidate area is analyzed by ULBP, and an exponential function with two parameters is utilized to model the texture of the flame area. According to the experimental results, our proposed method can reduce the number of false alarms greatly compared with an alternative algorithm, while ensuring the accurate classification of positive samples. The classification performance of our proposed method is proven to be better than that of alternative algorithms.

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References

  1. Rong, J., Zhou, D., Yao, W., Gao, W., Chen, J., Wang, J.: Fire flame detection based on GICA and target tracking. Opt. Laser Technol. 47, 283–291 (2013)

    Article  Google Scholar 

  2. Wang, D., Cui, X., Park, E., Jin, C., Kim, H.: Adaptive flame detection using randomness testing and robust features. Fire Saf. J. 55, 116–125 (2013)

    Article  Google Scholar 

  3. Borges, P.V.K., Izquierdo, E.: A probabilistic approach for vision-based fire detection in videos. IEEE Trans. Circuits Syst. Video Technol. 20, 721–731 (2010)

    Article  Google Scholar 

  4. Dedeoglu, Y., Töreyin, B.U., Güdükbay, U., Cetin, A.E.: Real-time fire and flame detection in video. In: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP2005), Philadelphia, Pennsylvania, USA, pp. 559–672 (2005)

  5. Ko, B.C., Cheong, K.H., Nam, J.Y.: Fire detection based on vision sensor and support vector machines. Fire Saf. J. 44, 322–329 (2009)

    Article  Google Scholar 

  6. Celik, T., Demirel, H., Ozkaramanli, H., Uyguroglu, M.: Fire detection using statistical color model in video sequences. J. Vis. Commun. Image Represent. 18, 176–185 (2007)

    Article  Google Scholar 

  7. Toreyin, B.U., Dedeoglu, Y., Cetin, A.E.: Flame detection in video using hidden Markov models. In: Proceedings of the IEEE International Conference on Image Processing (ICIP 2005), Genoa, Italy, pp. II-1230-3 (2005)

  8. Marbach, G., Loepfe, M., Brupbacher, T.: An image processing technique for fire detection in video images. Fire Saf. J. 41, 285–289 (2006)

    Article  Google Scholar 

  9. Günay, O., Taşdemir, K., Uǧur Töreyin, B., Enis Çetin, A.: Video based wildfire detection at night. Fire Saf. J. 44, 860–868 (2009)

    Article  Google Scholar 

  10. Ha, C., Hwang, U., Jeon,G., Cho, J., Jeong,J.: Vision-based fire detection algorithm using optical flow. In: Proceedings of the IEEE 6th International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS-2012), Palermo, Italy, pp. 526–530 (2012)

  11. Rinsurongkawong,S.t, Ekpanyapong, M., Dailey, M.N.: Fire detection for early fire alarm based on optical flow video processing. In: Proceedings of the IEEE 9th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON 2012), Hua Hin, Thailand, pp. 1–4 (2012)

  12. Lai, T.Y., Kuo, J.Y., FanJiang, Y.Y., Ma, S.P., Liao, Y.H.: Robust little flame detection on real-time video surveillance system. In: Proceedings of the IEEE 3th International Conference on Innovations in Bio-Inspired Computing and Applications (IBICA 2012), Taiwan, pp. 139–143 (2012)

  13. Töreyin, B.U., Dedeoǧlu, Y., Güdük bay, U., Enis Çetin, A.: Computer vision based method for real-time fire and flame detection. Pattern Recognit. Lett. 27, 49–58 (2006)

    Article  Google Scholar 

  14. Çelik, T., Demirel, H.: Fire detection in video sequences using a generic color model. Fire Saf. J. 44, 147–158 (2009)

    Article  Google Scholar 

  15. Habiboǧlu, Y.H., Günay, O., Enis Çetin, A.: Covariance matrix-based fire and flame detection method in video. Mach. Vis. Appl. 23, 1103–1113 (2012)

    Article  Google Scholar 

  16. Truong, T.X., Kim, J.M.: Fire flame detection in video sequences using multi-stage pattern recognition techniques. Eng. Appl. Artif. Intell. 25, 1365–1372 (2012)

    Article  Google Scholar 

  17. Chen, T.H., Wu, P.H., Chiou, Y.C.: An early fire-detection method based on image processing. In: Proceedings of the IEEE International Conference on Image Processing (ICIP2004), vol. 3, pp. 1707–1710 (2004)

  18. Jung, C., Kim, C.: A unified spectral-domain approach for saliency detection and its application to automatic object segmentation. IEEE Trans. Image Process. 21, 1272–1283 (2012)

    Article  MathSciNet  Google Scholar 

  19. Frintrop, S.: General object tracking with a component-based target descriptor. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA: 2010), Anchorage, AK, pp. 4531–4536 (2010)

  20. Santana, P., Mendonça, R., Correia, L., Barata, J.: Neural-swarm visual saliency for path following. Appl. Soft Comput. 13, 3021–3032 (2012)

    Article  Google Scholar 

  21. Itti, L., Koch, C.: A saliency-based search mechanism for overt and covert shifts of visual attention. Vision. Res. 1489–1506(40), 10–1 (2000)

    Google Scholar 

  22. Garcia-Diaz, A., Fdez-Vidal, X.R., Pardo, X.M., Dosil, R.: Saliency from hierarchical adaptation through decorrelation and variance normalization. Image Vis. Comput. 30, 51–64 (2012)

    Article  Google Scholar 

  23. Yang, B., Chen, S.: A comparative study on local binary pattern (LBP) based face recognition: LBP histogram versus LBP iImage. Neurocomputing 120, 365–379 (2013)

    Article  Google Scholar 

  24. Thanh Nguyen, D., Ogunbona, P.O., Li, W.: A novel shape-based non-redundant local binary pattern descriptor for object detection. Pattern Recognit. 46, 1485–1500 (2013)

    Article  Google Scholar 

  25. Pietikäinen, M.I, Hadid, A., Zhao, G., Ahonen, T.: Computer Vision Using Local Binary Patterns. Springer, London. ISBN 978-0-85729-747-1 (2011)

  26. Theodoridis, S., Koutroumbas, K.: Pattern Recognition, 4th edn. Elsevier. ISBN: 978-1-59749-272-0 (2008)

  27. Olson, D.L., Delen, D.: Advanced Data Mining Techniques. Springer, Berlin, Heidelberg. ISBN: 978-3-540-76917-0 (2008)

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Acknowledgments

This research is supported by the National Natural Science Foundation of China (Nos. 61103118 and 61203269), the Shandong Natural Science Foundation of China (No. BS2012DX027).

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Authors and Affiliations

  1. Shandong Provincial Key Laboratory of Digital Media Technology, School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan, China

    Zhao-Guang Liu & Xiu-Hua Ji

  2. School of Information Science and Engineering, Shandong University, Jinan, China

    Yang Yang

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  1. Zhao-Guang Liu
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  2. Yang Yang
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  3. Xiu-Hua Ji
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Correspondence to Zhao-Guang Liu.

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Liu, ZG., Yang, Y. & Ji, XH. Flame detection algorithm based on a saliency detection technique and the uniform local binary pattern in the YCbCr color space. SIViP 10, 277–284 (2016). https://doi.org/10.1007/s11760-014-0738-0

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  • DOI: https://doi.org/10.1007/s11760-014-0738-0

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