Skip to main content
SpringerLink
Log in

A Novel Efficient Video Smoke Detection Algorithm Using Co-occurrence of Local Binary Pattern Variants

  • Published:
Fire Technology Aims and scope Submit manuscript

Abstract

Smoke detection is an advance caution to the unforeseen great damage events. Therefore, it is required to identify the smoke in the course of initial stages for preventing fire events. A new technique is proposed to lessen the rate of incorrect alarm by identify the smoke and examine its distinctive texture attributes. Initially, the smoke-colored regions are segmented based on color at the YUV color locality. Then the tentative frame differencing is used to segment the candidate smoke region from the smoke-colored region. In the next phase, the candidate distinctive texture attributes in the smoke region are extracted using Co-occurrence of Hamming Distance based Local Binary pattern (CoHDLBP) and Co-occurrence of Local Binary pattern (CoLBP); these features include homogeneity, energy, correlation and contrast. Finally, the ELM classifier is proficient for the take-out features from the candidate smoke region, and then the decision has been taken with the assistance of a smoke alarm. Investigational outcomes proved that the suggested smoke recognition process executes better compared with all the usual smoke recognition methods by achieving better detection accuracy and processing time.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14

Similar content being viewed by others

References

  1. Enis Cetin A, Merci B, Günay O, Toreyin BU, Verstockt S (2016) Methods and techniques for fire detection. Elsevier, Amsterdam, pp 39–42

    Google Scholar 

  2. Favorskaya M, Pyataeva A, Popov A (2015) Verification of smoke detection in video sequences based on spatiotemporal local binary patterns. Procedia Comput Sci 60:671–680

    Article  Google Scholar 

  3. Lin G, Zhang Y, Zhang Q, Jia Y, Gao Xu, Wang J (2017) Smoke detection in video sequences based on dynamic texture using volume local binary patterns. KSII T Internet Info 11(11):5522–5536

    Google Scholar 

  4. Chongyuan Tao, Jian Zhang, Pan Wang (2010) Smoke Detection Based on Deep Convolutional Neural Networks. 2016 International Conference on Industrial Informatics - Computing Technology, Intelligent Technology, Industrial Information Integration. https://doi.org/10.1109/ICIICII.2016.0045

  5. Qureshi WS, Ekpanyapong M, Dailey MN, Rinsurongkawong S, Malenichev A, Krasotkina O (2016) QuickBlaze: Early Fire Detection Using a Combined Video Processing Approach. Fire Technol 52:1293–1317

    Article  Google Scholar 

  6. Emmy Prema C, Vinsley SS, Suresh S (2016) Multi Feature analysis of smoke in YUV color space for early forest fire detection. Fire Technol 52(5):1319–1342

    Article  Google Scholar 

  7. Yuan F, Shi J, Xia X, Fang Y, Fang Z, Mei T (2016) High-order local ternary patterns with locality preserving projection for smoke detection and image classification. Inf Sci 372(1):225–240

    Article  Google Scholar 

  8. Zhao Y, Zhou Z, Mingming X (2015) Forest fire smoke video detection using spatiotemporal and dynamic texture features. J Elect Comput E. https://doi.org/10.1155/2015/706187

    Article  Google Scholar 

  9. Emmy Prema C, Vinsley SS, Suresh S (2018) Efficient flame detection based on static and dynamic texture analysis in forest fire detection. Fire Technol 54:255–288

    Article  Google Scholar 

  10. Ye W, Zhao J, Wang S, Wang Y, Zhang D, Yuan Z (2015) Dynamic texture based smoke detection using surfacelet transform and HMT model. Fire Saf J 73:91–101

    Article  Google Scholar 

  11. Tian H, Li W, Ogunbona PO, Wang L (2018) Detection and separation of smoke from single image frames. IEEE Trans Image Process 27(3):1164–1177

    Article  MathSciNet  Google Scholar 

  12. Lin G, Zhang Q, Gao Xu, Qian L, Zhang Y (2015) Analysis of volume local binary patterns for video based smoke detection. Fire Saf J 73:91–101

    Article  Google Scholar 

  13. Jia Y, Yuan J, Wang J, Fang J, Zhang Y (2016) A Saliency-based method for early smoke detection in video sequences. Fire Technol 52:1271–1292

    Article  Google Scholar 

  14. Alamgir N, Nguyen K, Chandran V, Boles W (2018) Combining multi-channel color space with local binary co-occurrence feature descriptors for accurate smoke detection from surveillance videos. Fire Saf J 102:1–10

    Article  Google Scholar 

  15. Chaudhry T, Moinuddin K (2017) Method of identifying burning material from its smoke using attenuation of light. Fire Saf J 93:84–97

    Article  Google Scholar 

  16. Yuan F (2011) Video-based smoke detection with histogram sequence of LBP and LBPV pryamids. Fire Saf J 46:132–139

    Article  Google Scholar 

  17. Gao Xu, Zhang Y, Zhang Q, Lin G, Wang J (2017) Deep domain adaptation based video smoke detection using synthetic smoke images. Fire Saf J 93:53–59

    Article  Google Scholar 

  18. Bugaric M, Jakovcevic T, Stipanicev D (2014) Adaptive estimation of visual smoke detection parameters based on spatial data and fire risk index. Comput Vis Image Underst 118:184–196

    Article  Google Scholar 

  19. Chunyu Yu, Mei Z, Zhang Xi (2013) A real-time video fire flame and smoke detection algorithm. Procedia Eng 62:891–898

    Article  Google Scholar 

  20. Yuan F, Zhang L, Xia X, Wan B, Huang Q, Li X (2019) Deep smoke segmentation. Neurocomputing 357:248–260

    Article  Google Scholar 

  21. Emmy Prema C, Suresh S (2019) Local binary pattern based hybrid texture descriptors for the classification of smoke images. Int J Engg Res Technol 7(13):1–6

    Google Scholar 

  22. Zhang Q-X, Lin G-H, Zhang Y-M, Gao Xu, Wang J-J (2018) Wildland forest fire smoke detection based on faster R-CNN using synthetic smoke images. Procedia Eng 211:441–446

    Article  Google Scholar 

  23. Peng Y, Wang Yi (2019) Real-time forest smoke detection using hand-designed features and deep learning. Comput Electron Agr 167:105029. https://doi.org/10.1016/j.compag.2019.105029

    Article  Google Scholar 

  24. Tao H, Xiaobo Lu (2019) Smoke vehicle detection based on robust codebook model and robust volume local binary count patterns. Image Vision Comput 86:17–27

    Article  Google Scholar 

  25. Xua G, Zhanga Y, Zhanga Q, Lina G, Wangb Z, Jiac Y, Wanga J (2019) Video smoke detection based on deep saliency network. Fire Saf J 105:277–285

    Article  Google Scholar 

  26. Liu T, Cheng J, Xiangyu Du, Luo X, Zhang L, Cheng B, Wang Y (2019) Video smoke detection method based on change-cumulative image and fusion deep network. Sens 19(23):5060. https://doi.org/10.3390/s19235060

    Article  Google Scholar 

  27. Xuehui Wu, Xiaobo Lu, Leung H (2019) Video smoke separation and detection via sparse representation. Neurocomputing 360:61–74

    Article  Google Scholar 

  28. Zhou Z, Shi Y, Gao Z, Li S (2016) Wildfire smoke detection based on local extremal region segmentation and surveillance. Fire Saf J 85:50–58

    Article  Google Scholar 

  29. Thou-Ho (Chao-Ho) Chen, Yen-Hui Yin, Shi-Feng Huang and Yan-Ting Ye (2006) The Smoke Detection for Early Fire-Alarming System Base on Video Processing. In: IEEE International Conference on Intelligent Information Hiding and Multimedia Signal Processing. December 18 2006- December 20 2006. https://doi.org/10.1109/IIH-MSP.2006.265033

  30. Choi K, Toh KA, Byun H (2012) Incremental face recognition for large-scale social network services. Pattern Recogn 45(8):2868–2883. https://doi.org/10.1016/j.patcog.2012.02.002

    Article  Google Scholar 

  31. Minhas R, Mohammed A, Wu Q (2012) Incremental learning in human action recognition based on snippets. IEEE Trans Circuits Syst Video Technol 22(11):1529–1541. https://doi.org/10.1109/TCSVT.2011.2177182

    Article  Google Scholar 

  32. Kaya Y, Kayci L, Tekin R (2013) A computer vision system for the automatic identification of butterfly species via gabor filter-based texture features and extreme learning machine. TEM J 2(1):13–20

    Google Scholar 

  33. An L, Bhanu B (2012) Image super-resolution by extreme learning machine. In: IEEE international conference on image processing. Orlando, September 30 2012–October 3 2012, pp. 2209–2212.:https://doi.org/10.1109/ICIP.2012.6467333

Download references

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Bethlahem Institute of Engineering, Karungal, Tamil Nadu, 629157, India

    C. Emmy Prema

  2. Department of Mechanical Engineering, University College of Engineering, Nagercoil, Tamilnadu, 629 004, India

    S. Suresh

  3. Department of Mechanical Engineering, Amrita College of Engineering and Technology, Amritagiri, Erachakulam (Po), Nagercoil, 629 901, Tamilnadu, India

    M. Navaneetha Krishnan

  4. Department of Computer Science and Engineering, Chitkara University Institute of Engineering and Technology, Chitkara University, Chandigarh, Punjab, 140 401, India

    N. Leema

Authors
  1. C. Emmy Prema
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. Suresh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. M. Navaneetha Krishnan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. N. Leema
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to C. Emmy Prema.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Prema, C.E., Suresh, S., Krishnan, M.N. et al. A Novel Efficient Video Smoke Detection Algorithm Using Co-occurrence of Local Binary Pattern Variants. Fire Technol 58, 3139–3165 (2022). https://doi.org/10.1007/s10694-022-01306-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10694-022-01306-2

Keywords

Search

Navigation