Abstract
Recent technological advancement has opened the space for a gradual increase in the number of smart buildings. Public safety and security has becomes a matter of concern with such a development, especially in areas of fire accidents. The conventional fire detection system usually worked on sensors and takes time for fire detection. This work presents an early fire detection system that unlike conventional fire detection system is cost-effective with high fire detection rate. The proposed algorithm uses features like color, increase in area and intensity flicker for early detection of fire. Segmentation of fire colored regions is done with the help of L*a*b*, YCbCr, and RGB color space. Analysis of fire, i.e., fire area, its spread, temporal information, direction of the fire, and its average growth rate are measured using optical flow and blob analysis. Accuracy and F measure are used to evaluate the accuracy of the proposed system. Experimental results show that the average accuracy of the system is above 80% which is more promising in a video.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Celik T, Demirel H (2009) Fire detection in video sequences using a generic color model. Fire Saf J 44(2):147–158
Zhang Z, Zhao J, Zhang D, Qu C, Ke Y, Cai B (2008) Contour based forest fire detection using FFT and wavelet. In: 2008 International conference on computer science and software engineering, vol 1, pp 760–763
Celik T (2010) Fast and efficient method for fire detection using image processing. ETRI J 32(6):881–890
Zhang Z, Shen T, Zou J (2014) An improved probabilistic approach for fire detection in videos. Fire Technol 50(3):745–752
Kumar TS, Gautam KS, Haritha H (2016) Debris detection and tracking system in water bodies using motion estimation technique. In: Innovations in bio-inspired computing and applications, pp 275–284
Toreyin BU, Dedeoglu Y, Cetin AE (2005) Flame detection in video using hidden markov models. In: IEEE international conference on image processing, vol 2, pp II–1230
Bohush R, Brouka N (2013) Smoke and flame detection in video sequences based on static and dynamic features. In: Signal processing: algorithms, architectures, arrangements, and applications (SPA), pp 20–25
Chen T-H, Wu P-H, Chiou Y-C (2004) An early fire-detection method based on image processing. In: 2004 International conference on image processing, vol 3, pp 1707–1710
Yuan C, Liu Z, Zhang Y (2015) UAV-based forest fire detection and tracking using image processing techniques. In: International conference on unmanned aircraft systems, pp 639–643
Kecheril SS, Dr. Venkataraman D, Suganthi J, Sujathan K (2012) Segmentation of lung glandular cells using multiple color spaces. Int J Comput Sci Eng Appl 2(3):147–158
Stadler A, Windisch T, Diepold K (2014) Comparison of intensity flickering features for video based flame detection algorithms. Fire Saf J 66:1–7
Rinsurongkawong S, Ekpanyapong M, Dailey MN (2012) Fire detection for early fire alarm based on optical flow video processing. In: 9th International conference on electrical engineering/electronics, computer, telecommunications and information technology, pp 1–4
Kim Y-H, Kim A, Jeong H-Y (2014) RGB color model based the fire detection algorithm in video sequences on wireless sensor network. Int J Distrib Sens Netw 10(4)
Toulouse T, Rossi L, Akhloufi M, Celik T, Maldague X (2015) Benchmarking of wildland fire colour segmentation algorithms. IET Image Process 9(12):1064–1072
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Paresh, P.A., Parameswaran, L. (2019). Vision-Based Algorithm for Fire Detection in Smart Buildings. In: Pandian, D., Fernando, X., Baig, Z., Shi, F. (eds) Proceedings of the International Conference on ISMAC in Computational Vision and Bio-Engineering 2018 (ISMAC-CVB). ISMAC 2018. Lecture Notes in Computational Vision and Biomechanics, vol 30. Springer, Cham. https://doi.org/10.1007/978-3-030-00665-5_99
Download citation
DOI: https://doi.org/10.1007/978-3-030-00665-5_99
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-00664-8
Online ISBN: 978-3-030-00665-5
eBook Packages: EngineeringEngineering (R0)