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Fire and Smoke Dynamic Textures Characterization by Applying Periodicity Index Based on Motion Features

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Computer Vision Systems (ICVS 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10528))

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Abstract

Dynamic texture has been described as images sequence that demonstrates continuous movement of pixels intensity change patterns in time. We consider the motion features of smoke and fire dynamic textures, which are important for fire calamity surveillance system to analyze the fire situation. We propose a method to understand the motion of intensity change. The objective is not only for classification purpose but also to characterize the motion pattern of fire and smoke dynamic texture. The radius of vector usually describes how fast the intensity change. The motion coherence index has been developed to assess the motion coherency between observed vector and its neighborhoods. We implement strategic motion coherence analysis to determine the motion coherence index of motion vector field in each video frame. In practical, both covariance stationarity of average radius and motion coherence index are efficiently used to investigate fire and smoke characteristics by applying periodicity index for analysis.

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References

  1. Doretto, G.: Dynamic textures: modeling, learning, synthesis, animation, segmentation, and recognition. Thesis, University of California (2005)

    Google Scholar 

  2. Tsai, J.C., Shih, T.K., Wattanachote, K., Li, K.C.: Video editing using motion inpainting. In: 26th IEEE International Conference on Advanced Information Networking and Applications, pp. 649–654 (2012)

    Google Scholar 

  3. Wattanachote, K., Shih, T.K.: Automatic dynamic texture transformation based on a new motion coherence metric. IEEE Trans. Circuits Syst. Video Technol. 26(10), 1805–1820 (2016)

    Article  Google Scholar 

  4. Dmitry, C., Sandor, F.: On motion periodicity of dynamic textures. In: British Machine Vision Conference, vol. 1, pp. 167–176 (2006)

    Google Scholar 

  5. Wattanachote, K., Li, K., Wang, Y., Shih, T.K., Liu, W.Y.: Preliminary investigation on stationarity of dynamic smoke texture and dynamic fire texture based on motion coherent metric. In: International Conference on Machine Vision and Information Technology, pp. 99–104 (2017)

    Google Scholar 

  6. Deretto, G., Chiuso, A., Wu, Y.N., Soatto, S.: Dynamic textures. Int. J. Comput. Vis. 51(2), 91–109 (2003)

    Article  MATH  Google Scholar 

  7. Soatto, S., Doretto, G., Wu, Y.N.: Dynamic textures. In: IEEE International Conference on Computer Vision, vol. 2, pp. 439–446 (2001)

    Google Scholar 

  8. Chan, S.C., Zhang, Z.G.: Local polynomial modeling and variable bandwidth selection for time-varying linear systems. IEEE Trans. Instrum. Measur. 60(3), 1102–1117 (2011)

    Article  Google Scholar 

  9. Zhang, Z.G., Hung, Y.S., Chan, S.C.: Local polynomial modelling of time-varying autoregressive models with application to time-frequency analysis of event-related EEG. IEEE Trans. Biomed. Eng. 58(3), 557–566 (2011)

    Article  Google Scholar 

  10. Chen, C.I., Chen, Y.C.: Comparative study of harmonic and interharmonic estimation methods for stationary and time-varying signals. IEEE Trans. Industr. Electron. 61(1), 397–404 (2014)

    Article  Google Scholar 

  11. Zhang, Z.G., Chan, S.C., Wang, C.: A new regularized adaptive windowed lomb-periodogram for time-frequency analysis of non-stationary signals with impulsive components. IEEE Trans. Instrum. Measur. 61(8), 2283–2304 (2012)

    Article  Google Scholar 

  12. Saisan, P., Doretto, G., Wu, Y., Soatto, S.: Dynamic texture recognition. In: IEEE International Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 58–63 (2001)

    Google Scholar 

  13. Chen, T., Kao, C., Chang, S.: An intelligent real-time fire-detection method based on video processing. In: The International Carnahan Conference on Security Technology, pp. 104–111 (2003)

    Google Scholar 

  14. Ha, C., Jeon, G., Jeon, J.: Vision-based smoke detection algorithm for early fire recognition in digital video recording system. In: 7th International Conference on Signal Image Technology and Internet-Based Systems, pp. 209–212 (2011)

    Google Scholar 

  15. Farneback, G.: Fast and accurate motion estimation using orientation tensors and parametric motion models. In: 15th International Conference on Pattern Recognition, vol. 1, pp. 135–139 (2000)

    Google Scholar 

  16. Farnebäck, G.: Two-frame motion estimation based on polynomial expansion. In: Bigun, J., Gustavsson, T. (eds.) SCIA 2003. LNCS, vol. 2749, pp. 363–370. Springer, Heidelberg (2003). doi:10.1007/3-540-45103-X_50

    Chapter  Google Scholar 

  17. Rasmussen, C.E., Williams, C.K.I.: Gaussian Processes for Machine Learning. MIT Press, Cambridge (2006). Chapter 4

    MATH  Google Scholar 

  18. Franses, P.H.: Time Series Models for Business and Economic Forecasting. Cambridge University Press, Cambridge (1998)

    Google Scholar 

  19. Liu, H.X., Li, J., Zhao, Y., Qu, L.S.: Improved singular value decomposition technique for detecting and extracting periodic impulse component in a vibration signal. Chin. J. Mech. Eng. 17(3), 340–345 (2004)

    Article  Google Scholar 

  20. Kanjilal, P.P., Palit, S.: The singular value decomposition-applied in modelling and prediction of quasiperiodic processes. IEEE Trans. Sig. Process. 43(6), 1536–1540 (1995)

    Article  MATH  Google Scholar 

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Acknowledgments

This work was partially supported by the Guangdong Innovative Research Team Program (No. 2014ZT05G157).

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Correspondence to Kanoksak Wattanachote .

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Wattanachote, K., Lin, Z., Jiang, M., Li, L., Wang, G., Liu, W. (2017). Fire and Smoke Dynamic Textures Characterization by Applying Periodicity Index Based on Motion Features. In: Liu, M., Chen, H., Vincze, M. (eds) Computer Vision Systems. ICVS 2017. Lecture Notes in Computer Science(), vol 10528. Springer, Cham. https://doi.org/10.1007/978-3-319-68345-4_45

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  • DOI: https://doi.org/10.1007/978-3-319-68345-4_45

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68344-7

  • Online ISBN: 978-3-319-68345-4

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