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International Conference on Computer Science, Engineering and Education Applications

ICCSEEA 2019: Advances in Computer Science for Engineering and Education II pp 172–182Cite as

Real-Time Flame Detection Using Hypotheses Generating Techniques

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 938))

Abstract

Nowadays the real-time object tracking in video streams is an important problem in smart city applications. One of the problem of smart city monitoring such as the flame detection task in real time is considered. The flame as the object of interest is one of the most complex objects tracking. This is due to the lack of time and space invariance in all features of the object of interest. In accordance with this fact, the computational processes are characterized by high computational complexity and latency of program tracking procedures. There are several basic approaches to minimizing computing costs and reducing time delays (shifts). Using the ROI is one of them. Thus, a new model of the hypothesis generator for the effective evaluation of ROI is proposed.

The task of the generator is to select the areas that contain a fire in the video stream. In this case, the main criterion for identifying the region is the index of complexitivity. Due to using the proposed model, the classification procedures quality is increased, and as a result, computing costs of the tracking methods is reduced in general. The many experiments based on both benchmarks and real data sets have confirmed the effectiveness of the proposed approach.

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References

  1. Chang, K., Liu, P., Yu, C.: Design of real-time video streaming and object tracking system for home care services. In: Proceedings of the 2016 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW), Nantou, pp. 1–2 (2016). https://doi.org/10.1109/icce-tw.2016.7521004

  2. Paul, G.V., Beach G.J., Cohen, C.J.: A realtime object tracking system using a color camera. In: Proceedings of the 30th Applied Imagery Pattern Recognition Workshop (AIPR 2001). Analysis and Understanding of Time Varying Imagery, Washington, DC, USA, pp. 137–142 (2001). https://doi.org/10.1109/aipr.2001.991216

  3. Hung, M., Chang, C., Chen, J., Lin, J., Liao, T.: Design and implementation of real-time object tracking system using the Gaussian motion model and the otsu algorithm. In: Proceedings of the 2009 Second International Symposium on Knowledge Acquisition and Modeling, Wuhan, pp. 140–143 (2009). https://doi.org/10.1109/kam.2009.106

  4. Lyu, C., Chen, H., Jiang, X., Li, P., Liu, Y.: Real-time object tracking system based on field-programmable gate array and convolution neural network. Int. J. Adv. Robot. Syst. 14(1), 17–29 (2017). https://doi.org/10.1177/1729881416682705

    Article  Google Scholar 

  5. Růžička, M., Mašek, P.: Real time object tracking based on computer vision. In: Březina T., Jabloński R. (eds.) Mechatronics 2013, pp. 591–598. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-02294-9_75

  6. Shen, D., Chen, X., Nguyen, M., Yan, W.Q.: Flame detection using deep learning. In: Proceedings of the 2018 4th International Conference on Control, Automation and Robotics (ICCAR), Auckland, pp. 416–420 (2018). https://doi.org/10.1109/iccar.2018.8384711

  7. Muhammad, K., Ahmad, J., Mehmood, I., Rho, S., Baik, S.W.: Convolutional neural networks based fire detection in surveillance videos. IEEE Access 6, 18174–18183 (2018). https://doi.org/10.1109/ACCESS.2018.2812835

    Article  Google Scholar 

  8. Kim, O., Kang, D.-J.: Fire detection system using random forest classification for image sequences of complex background. Opt. Eng. 52(6) (2013). https://doi.org/10.1117/1.oe.52.6.067202

  9. Peleshko, D., Maksymiv, O., Rak, T., Voloshyn, O., Morklyanyk, B.: Core generator of hypotheses for real-time flame detecting. In: Proceedings of the 2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP), Lviv, Ukraine, pp. 455–458 (2018). https://doi.org/10.1109/dsmp.2018.8478446

  10. binti Zaidi, N.I., binti Lokman, N.A.A., bin Daud, M.R., Achmad, H., Chia, K.A.: Fire recognition using RGB and YCBCR color space. ARPN J. Eng. Appl. Sci. 10(21), 9786–9790 (2015)

    Google Scholar 

  11. Patel, P., Tiwari, S.: Flame detection using image processing techniques. Int. J. Comput. Appl. 58(18), 13–16 (2012). https://doi.org/10.5120/9381-3817

    Article  Google Scholar 

  12. Chen, T.-H., Kao, Ch.-L., Chang, S.-M.: An intelligent real-time fire-detection method based on video processing. In: Proceedings of the IEEE 37th Annual 2003 International Carnahan Conference on Security Technology, Taipei, Taiwan, pp. 104–111 (2003). https://doi.org/10.1109/ccst.2003.1297544

  13. Rossi, L., Akhloufi, M., Tison, Y.: On the use of stereovision to develop a novel instrumentation system to extract geometric fire fronts characteristics. Fire Saf. J. 46(1–2), 9–20 (2011). https://doi.org/10.1016/j.firesaf.2010.03.001

    Article  Google Scholar 

  14. Prema, C.E., Vinsley, S.S., Suresh, S.: Multi feature analysis of smoke in YUV color space for early forest fire detection. Fire Technol. 52(5), 1319–1342 (2016). https://doi.org/10.1007/s10694-016-0580-8

    Article  Google Scholar 

  15. Çelik, T., Özkaramanlı, H., Demirel H.: Fire and smoke detection without sensors: image processing based approach. In: Proceedings of the 15th European Signal Processing Conference, Poznan, Poland, pp. 1794–1798 (2007). ISBN 978-839-2134-04-6

    Google Scholar 

  16. (Chao-Ho) Chen, T.-H., Wu, P.-H., Chiou, Y.-C.: An early fire-detection method based on image processing. In: Proceedings of the International Conference on Image Processing (ICIP), vol. 3, pp. 1707–1710 (2004). https://doi.org/10.1109/icip.2004.1421401

  17. Celik, T.: Fast and efficient method for fire detection using image processing. ETRI J. 32(6), 881–890 (2010). https://doi.org/10.4218/etrij.10.0109.0695

    Article  Google Scholar 

  18. Rinsurongkawong, S., Ekpanyapong, M., Dailey, M.N.: Fire detection for early fire alarm based on optical flow video processing. In: Proceedings of the 9th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, Phetchaburi, Thailand, pp. 1–4 (2012). https://doi.org/10.1109/ecticon.2012.6254144

  19. Guruh, F.S., Fajrian, N.A., Catur, S., Ricardus, A.P., Pulung, N.A.: Multi color feature, background subtraction and time frame selection for fire detection. In: Proceedings of the 2013 International Conference on Robotics, Biomimetics, Intelligent Computational Systems, Jogjakarta, Indonesia, pp. 115–120 (2013). https://doi.org/10.1109/robionetics.2013.6743589

  20. Toreyin, B., Dedeoglu, Y., Cetin, A.: Flame detection in video using hidden Markov models. In: Proceedings of the IEEE International Conference on Image Processing, Genova, Italy, vol. 2, pp. 213–216 (2005). https://doi.org/10.1109/icip.2005.1530284

  21. Liu, Z.-G., Yang, Y., Ji, X.-H.: Flame detection algorithm based on a saliency detection technique and the uniform local binary pattern in the YCbCr color space. Signal, Image Video Process. 10(2), 277–284 (2016). https://doi.org/10.1007/s11760-014-0738-0

    Article  Google Scholar 

  22. Toreyin, B.U., Dedeoglu, Y., Cetin, A.E.: Wavelet based real-time smoke detection in video. In: Proceedings of the 13th European Signal Processing Conference, Antalya, Turkey, pp. 1–4 (2005). ISBN 978-160-4238-21-1

    Google Scholar 

  23. Araya, L.V., Espada, N., Tosini, M., Leiva, L.: Simple detection and classification of road lanes based on image processing. Int. J. Inf. Technol. Comput. Sci. (IJITCS) 10(8), 38–45 (2018). https://doi.org/10.5815/ijitcs.2018.08.06

    Article  Google Scholar 

  24. Hu, Z., Dychka, I., Sulema, Y., Valchuk, Y., Shkurat, O.: Method of medical images similarity estimation based on feature analysis. Int. J. Intell. Syst. Appl. (IJISA) 10(5), 14–22 (2018). https://doi.org/10.5815/ijisa.2018.05.02

    Article  Google Scholar 

  25. Chaudhary, A.S., Chaturvedi, D.K.: Analyzing defects of solar panels under natural atmospheric conditions with thermal image processing. Int. J. Image, Graph. Signal Process. (IJIGSP) 10(6), 10–21 (2018). https://doi.org/10.5815/ijigsp.2018.06.02

    Article  Google Scholar 

  26. Al-Ameen, Z., Zaman, A.H.: A low-complexity algorithm for contrast enhancement of digital images. Int. J. Image Graph. Signal Process. (IJIGSP) 10(2), 60–67 (2018). https://doi.org/10.5815/ijigsp.2018.02.07

    Article  Google Scholar 

  27. Gourav, Sharma, T., Singh, H.: Computational approach to image segmentation analysis. Int. J. Mod. Educ. Comput. Sci. (IJMECS), 9(7), 30–37 (2017). https://doi.org/10.5815/ijmecs.2017.07.04

  28. Al-Othman, A.B., Al-Ameen, Z., Sulong, G.B.: Improving the MRI tumor segmentation process using appropriate image processing techniques. Int. J. Image, Graph. Signal Process. (IJIGSP) 6(2), 23–29 (2014). https://doi.org/10.5815/ijigsp.2014.02.03

    Article  Google Scholar 

  29. Youssef, K., Woo, P.-Y.: Difference of the absolute differences – A new method for motion detection. Int. J. Intell. Syst. Appl. (IJISA) 4(9), 1–14 (2012). https://doi.org/10.5815/ijisa.2012.09.01

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. IT Step University, 83a Zamarstynivs’ka Street, Lviv, Ukraine

    Dmytro Peleshko, Olena Vynokurova & Semen Oskerko

  2. Lviv State University of Life Safety, 35, Kleparivska Street, Lviv, Ukraine

    Oleksii Maksymiv

  3. Lviv National Polytechnic University, 12 S.Bandery Street, Lviv, Ukraine

    Orysia Voloshyn

Authors
  1. Dmytro Peleshko
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  2. Olena Vynokurova
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  3. Semen Oskerko
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  4. Oleksii Maksymiv
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  5. Orysia Voloshyn
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Corresponding author

Correspondence to Olena Vynokurova .

Editor information

Editors and Affiliations

  1. School of Educational Information Technology, Central China Normal University, Wuhan, Hubei, China

    Zhengbing Hu

  2. Mechanical Engineering Research Institute of the Russian Academy of Sciences, Moscow, Russia

    Sergey Petoukhov

  3. “Igor Sikorsky Kiev Polytechnic Institute”, National Technical University of Ukraine, Kiev, Ukraine

    Ivan Dychka

  4. Halmos College of Natural Sciences and Oceanography, Nova Southeastern University, Ft. Lauderdale, FL, USA

    Matthew He

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Peleshko, D., Vynokurova, O., Oskerko, S., Maksymiv, O., Voloshyn, O. (2020). Real-Time Flame Detection Using Hypotheses Generating Techniques. In: Hu, Z., Petoukhov, S., Dychka, I., He, M. (eds) Advances in Computer Science for Engineering and Education II. ICCSEEA 2019. Advances in Intelligent Systems and Computing, vol 938. Springer, Cham. https://doi.org/10.1007/978-3-030-16621-2_16

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