An Early Flame Detection System Based on Image Block Threshold Selection Using Knowledge of Local and Global Feature Analysis

Ting Wei Hsu, Shreya Pare, Mahendra Singh Meena, Deepak Kumar Jain, Dong Lin Li, Amit Saxena, Mukesh Prasad and Chin Teng Lin
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Ting Wei Hsu: Department of Electrical Engineering, National Chiao Tung University, Hsinchu 30010, Taiwan
Shreya Pare: School of Computer Science, FEIT, University of Technology Sydney, Ultimo 2007, Sydney, Australia
Mahendra Singh Meena: School of Computer Science, FEIT, University of Technology Sydney, Ultimo 2007, Sydney, Australia
Deepak Kumar Jain: Institute of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
Dong Lin Li: Department of Electrical Engineering, National Taiwan Ocean University, Keelung 202301, Taiwan
Amit Saxena: Department of Computer Science and Information Technology, Guru Ghashidash University, Bilaspur, Chhattisgarh 495009, India
Mukesh Prasad: School of Computer Science, FEIT, University of Technology Sydney, Ultimo 2007, Sydney, Australia
Chin Teng Lin: School of Computer Science, FEIT, University of Technology Sydney, Ultimo 2007, Sydney, Australia

Sustainability, 2020, vol. 12, issue 21, 1-22

Abstract: Fire is one of the mutable hazards that damage properties and destroy forests. Many researchers are involved in early warning systems, which considerably minimize the consequences of fire damage. However, many existing image-based fire detection systems can perform well in a particular field. A general framework is proposed in this paper which works on realistic conditions. This approach filters out image blocks based on thresholds of different temporal and spatial features, starting with dividing the image into blocks and extraction of flames blocks from image foreground and background, and candidates blocks are analyzed to identify local features of color, source immobility, and flame flickering. Each local feature filter resolves different false-positive fire cases. Filtered blocks are further analyzed by global analysis to extract flame texture and flame reflection in surrounding blocks. Sequences of successful detections are buffered by a decision alarm system to reduce errors due to external camera influences. Research algorithms have low computation time. Through a sequence of experiments, the result is consistent with the empirical evidence and shows that the detection rate of the proposed system exceeds previous studies and reduces false alarm rates under various environments.

Keywords: feature extraction; video surveillance; image processing; fire detection; block-based analysis (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
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