A study on the algorithm for fire recognition for automatic forest fire detection: The International Conference on Control, Automation and Systems 2010 (ICCAS 2010)

Choi, Ho-Woong; Min, Inkyu; Oh, Eui-Seok; Park, Dong‐Ho

doi:10.1109/iccas.2010.5670155

Iccas 2010 2010

DOI: 10.1109/iccas.2010.5670155

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A study on the algorithm for fire recognition for automatic forest fire detection: The International Conference on Control, Automation and Systems 2010 (ICCAS 2010)

Ho-Woong Choi

Inkyu Min

Eui-Seok Oh

et al.

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Cited by 2 publications

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Automatic video fire detection approach based on PJF color modeling and spatio-temporal analysis

Daoud¹,

Hamida²,

Amar³

2019

JWSCG

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Recently, due to the huge damage caused by fires in many countries in the world, fire detection is getting more and more interest as an increasing important issue. Nowadays, the early fire detection in video surveillance scenes is emerging as an alternative solution to overcome the shortcomings of the current inefficient sensors. In this paper, we propose a new video based-fire detection method exploiting color and motion information of fire. Our approach consists in detecting all moving regions in the scene to select then areas likely to be fire. Further, motion analysis is required to identify the accurate fire regions. The proposed method is evaluated on different video datasets containing diverse fire and non-fire videos. Experimental results demonstrate the effectiveness of our proposed method by achieving high fire detection and low false alarms rates. Moreover, it greatly outperforms the related works with 98.81 % accuracy and only 2 % of false positive rate.

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Automatic video fire detection approach based on PJF color modeling and spatio-temporal analysis

Daoud¹,

Hamida²,

Amar³

2019

JWSCG

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Copyright Protection for Fire Video Images using an Effective Watermarking Method

Nguyen¹,

Kim²

2013

KIPS Transactions on Software and Data Engineering

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This paper proposes an effective watermarking approach for copyright protection of fire video images. The proposed watermarking approach efficiently utilizes the inherent characteristics of fire data with respect to color and texture by using a gray level co-occurrence matrix (GLCM) and fuzzy c-means (FCM) clustering. GLCM is used to generate a texture feature dataset by computing energy and homogeneity properties for each candidate fire image block. FCM is used to segment color of the fire image and to select fire texture blocks for embedding watermarks. Each selected block is then decomposed into a one-level wavelet structure with four subbands [LL, LH, HL, HH] using a discrete wavelet transform (DWT), and LH subband coefficients with a gain factor are selected for embedding watermark, where the visibility of the image does not affect. Experimental results show that the proposed watermarking approach achieves about 48 dB of high peak-signal-to-noise ratio (PSNR) and 1.6 to 2.0 of low M-singular value decomposition (M-SVD) values. In addition, the proposed approach outperforms conventional image watermarking approach in terms of normalized correlation (NC) values against several image processing attacks including noise addition, filtering, cropping, and JPEG compression.

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A study on the algorithm for fire recognition for automatic forest fire detection: The International Conference on Control, Automation and Systems 2010 (ICCAS 2010)

Cited by 2 publications

References 7 publications

Automatic video fire detection approach based on PJF color modeling and spatio-temporal analysis

Automatic video fire detection approach based on PJF color modeling and spatio-temporal analysis

Copyright Protection for Fire Video Images using an Effective Watermarking Method

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