Efficient Fire Pixel Segmentation Using Color Models in Still Images

Authors

  • Vadivu MS MCA Dept, Jyoti Nivas, College, Bangalore, India
  • Vijayalakshmi MN MCA Dept, R.V Engineering College, Bangalore, India

DOI:

https://doi.org/10.26438/ijcse/v6i9.2328

Keywords:

Object detection, Color Spaces, Thresholding, Segmentation

Abstract

Forest Fire causes more disasters to the environment. Detecting the fire in the early stage will play a crucial role to prevent the risky effects. The vision-based approaches have gained more impact than the conventional fire detection methods with respect to accuracy and less false alarms. A reliable and efficient computer vision based technique to retrieve fire-colored pixels in still images is proposed in this article. It adopts both RGB and L*a*b* space for segmenting the fire-colored pixels on colour feature. The proposed results are compared with the current methods. The results of proposed method bring satisfactory results than the existing techniques.

References

[1] Arun and Santhosh, "Lab Color Space Model with Optical Flow Estimation for Fire Detection in Videos ", IOSR Journal of Computer Engineering, 2014, pp 23-28.

[2] B.U. Toreyin, Y. Dedeoglu, and A.E. Cetin, “Computer Vision Based Method for Real-Time Fire and Flame Detection”, Pattern Recognition Letters, vol. 27, no. 1, 2006, pp. 49-58.

[3] B.U. Toreyin, Y. Dedeoglu, and A.E. Cetin, “Flame Detection in Video Using Hidden Markov Models”, Proc. IEEE Int. Conf. Image Process. 2005, pp. 1230-1233, 2005.

[4] Daniel Y. T. Chino, Letricia P. S. Avalhais, Jose F. Rodrigues Jr., Agma J. M. Traina BoWFire: Detection of Fire in Still Images by Integrating Pixel Color and Texture Analysis, Proceedings of the 28th SIBGRAPI Conference on Graphics, Patterns and Images, 2015.

[5] Habiboˇglu, Y.H., G¨unay, O., C¸ etin, A.E., “Covariance matrix-based fire and flame detection method in video”, Machine Vision and Applications 23(6), 11031113 (2011).

[6] Hira Lal Gope*1, Machbah Uddin2, Shohag Barman3, Dilshad Islam4, Dr. Mohammad Khairul Islam5, "Fire Detection in Still Image Using Color Model " ,Indonesian Journal of Electrical Engineering and Computer Science Vol. 3, No. 3, September 2016, pp. 618 ~ 625.

[7] J. Zhao, Z. Zhang, S. Han, C. Qu, Z. Yuan, and D. Zhang, “Svm based forest fire detection using static and dynamic features,” Computer Science and Information Systems, vol. 8, no. 3, pp. 821–841, 2011.

[8] J.Liu, W, "Early fire detection in coalmine based on video processing" Advance in Intelligent Systems and Computing, vol.181,, 239-245, 2013.Kumarguru Poobalan1 and Siau-Chuin Liew2, "Fire Detection algorithm using Image Processing Techniques" , Proceeding of the 3rd International Conference on Artificial Intelligence and Computer Science (AICS2015), 12 - 13 October 2015 pp 160-168.

[9] Rossi, L., Akhloufi, M., Tison, Y, " On the use of stereovision to develop a novel instrumentation system to extract geometric fire fronts characteristics”, Fire Safety Journal 46, 920 (2011).

[10] Rudz, S., Chetehouna, K., Hafiane, A., Laurent, H., Sero- Guillaume, O., " Investigation of a novel image segmentation method dedicated to forest fire applications" , In: Measurement Science and Technology 24(7), pp.075403 (2013).

[11] T. Celik et al., “Fire Detection Using Statistical Color Model in Video Sequences,” J. Visual Commun. Image Representation, vol. 18, no. 2, Apr 2007, pp. 176-185.

[12] T. Celik, H. Demirel, and H. Ozkaramanli, “Automatic Fire Detection in Video Sequences,” Proc. European Signal

Process. Conf., Florence, Italy, Sept. 2006.

[13] T.-H. Chen, P.-H. Wu, and Y.-C. Chiou, “An early fire-detection method based on image processing,” in ICIP, vol. 3, 2004, pp. 1707–1710.

[14] Tom Toulouse, Lucile Rossi, Turgay Celik, Moulay Akhlou, "Automatic fire pixel detection using image processing: A comparative analysis of Rule-based and Machine Learning-based methods", Signal Image and Video Processing, 2015, pp.1863-1703.

[15] Turgay Celik, "Fast and Efficient Method for Fire Detection Using Image Processing ", ETRI Journal, Volume 32, Number 6, December 2010.

[16] W. Phillips III, M. Shah, and N. da Vitoria Lobo, “Flame Recognition in Video,” Proc. 5th Workshop Appl. Computer Vision, 2000, pp. 224- 229.

[17] YH Kim, A Kim, HY Jeong, “RGB color model based the fire detection algorithm in video sequences on wireless sensor network ", Int. J. Distrib. Sensor Netw. 2014.

[18] V. Vipin. “Image Processing Based Forest Fire Detection”, IJETAE, 2(2012) 87-95.

[19] C. Emmy Prema*,S. S. Vinsley, S. Suresh,” Efficient Flame Detection Based on Static and Dynamic Texture Analysis in Forest Fire Detection”, Fire Technology Journal , 54, 255–288, 2018

[20] Pasquale Foggia,” Real-time Fire Detection for Video Surveillance Applications using a Combination of Experts based on Color, Shape and Motion”, IEEE Transactions on Circuits and Systems for Video Technology •, pp 1-12, January 2015.

Downloads

Published

2018-09-30
CITATION
DOI: 10.26438/ijcse/v6i9.2328
Published: 2018-09-30

How to Cite

[1]
M. vadivu and M. Vijayalakshmi, “Efficient Fire Pixel Segmentation Using Color Models in Still Images”, Int. J. Comp. Sci. Eng., vol. 6, no. 9, pp. 23–28, Sep. 2018.

Issue

Vol. 6 No. 9 (2018): IJCSE September Editor

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.