• The Journal of the Korea institute of electronic communication sciences
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• v.6 no.4
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• pp.479-485
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• 2011

A video flame detection method analyze the temporal and spatial characteristics of the regions which have the flame-like color and moving objects in the input video. The video flame detector should be able to reduce a false alarm rate without the degradation of flame detection capability. The conventional methods can reject the false alarm caused by the car lights and some electric lights. However they make the false alarm caused by the warning lights, neon sign, and some periodic flickering lights which have the flame-like color and temporal features. This paper propose the video flame detection method with periodicity analysis based false alarm rejection. The proposed method can detect the periodicity of the flickering electric lights and can reject the false alarm caused by the periodic electric lights. The computer simulation showed that the proposed method did not make the false alarm in the test video with the periodic electric lights. But the conventional methods made a false alarm in the same test video.

• Journal of Satellite, Information and Communications
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• v.8 no.4
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• pp.89-94
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• 2013

This paper presents color video flame detection algorithm based on wavelet transform to remove detection of flickering non-flame objects. Conventional flame detection algorithms consist of simple or mixed functions using colors, temporal and spatial characteristics. But those algorithms detect non-flame objects as flame regions sometimes. False alarm reasons are flame-like objects with regular flickering lights such as car signal lamps, alarm lights etc. The proposed algorithm is to reduce false detection which is occurred in periodic flickering lights. At first, It segments the candidate flame regions by using frame difference, flame colors. Then it distinguish flame regions and non flame regions including flickering car lights by analyzing wavelet coefficients. Computer simulation results showed that the proposed algorithm removes false detection due to the periodic flickering lamps by performing 97.9% of correct detection rate while false detection rate is 7.3%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.10
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• pp.3475-3489
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• 2014

Here we present a simple flame detection method for an infrared (IR) thermal camera based real-time fire surveillance digital signal processor (DSP) system. Infrared thermal cameras are especially advantageous for unattended fire surveillance. All-weather monitoring is possible, regardless of illumination and climate conditions, and the data quantity to be processed is one-third that of color videos. Conventional IR camera-based fire detection methods used mainly pixel-based temporal correlation functions. In the temporal correlation function-based methods, temporal changes in pixel intensity generated by the irregular motion and spreading of the flame pixels are measured using correlation functions. The correlation values of non-flame regions are uniform, but the flame regions have irregular temporal correlation values. To satisfy the requirement of early detection, all fire detection techniques should be practically applied within a very short period of time. The conventional pixel-based correlation function is computationally intensive. In this paper, we propose an IR camera-based simple flame detection algorithm optimized with a compact embedded DSP system to achieve early detection. To reduce the computational load, block-based calculations are used to select the candidate flame region and measure the temporal motion of flames. These functions are used together to obtain the early flame detection algorithm. The proposed simple algorithm was tested to verify the required function and performance in real-time using IR test videos and a real-time DSP system. The findings indicated that the system detected the flames within 5 to 20 seconds, and had a correct flame detection ratio of 100% with an acceptable false detection ratio in video sequence level.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.12
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• pp.5485-5506
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• 2016

In this article, a video based fire detection framework for CCTV surveillancesystems is presented. Two novel features and a novel image type with their corresponding algorithmsareproposed for this purpose. One is for the slow-smoke detection and another one is for fast-smoke/flame detection. The basic idea is slow-smoke has a highly varying chrominance/luminance texture in long periods and fast-smoke/flame has a highly varying texture waiting at the same location for long consecutive periods. Experiments with a large number of smoke/flame and non-smoke/flame video sequences outputs promising results in terms of algorithmic accuracy and speed.

• Journal of Satellite, Information and Communications
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• v.9 no.4
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• pp.91-96
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• 2014

This paper proposed an adaptive threshold method for detecting flame candidate regions in a infrared image and it adapts according to the contrast and intensity changes in the image. Conventional flame detection systems uses fixed threshold method since surveillance environment does not change, once the system installed. But it needs a adaptive threshold method as requirements of surveillance system has changed. The proposed adaptive threshold algorithm uses the dynamic behavior of flame as featured parameter. The test result is analysed by comparing test result of proposed adaptive threshold algorithm and conventional fixed threshold method. The analysed data shows, the proposed method has 91.42% of correct detection rate and false detection is reduced by 20% comparing to the conventional method.

• Proceedings of the Korea Institute of Fire Science and Engineering Conference
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• 2008.04a
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• pp.135-138
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• 2008

Many Victims and property damage are caused in fires. In this paper, an flame detection algorithm is proposed to early alarm fires. The proposed flame detection algorithm is based on 2-stage decision strategy of video processing. The first decision is to check with color distribution of input vidoe. In the second, the candidated region is settled as fire region with activity. As a result of simulation, it is shown that the proposed algorithm is useful for fire recognition.

• The KIPS Transactions:PartB
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• v.16B no.5
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• pp.367-376
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• 2009

In this paper, we propose a flame detection method using Haar wavelet and moving averages in outdoor infrared video sequences. Our proposed method is composed of three steps which are Haar wavelet decomposition, flame candidates detection, and their tracking and flame classification. In Haar wavelet decomposition, each frame is decomposed into 4 sub- images(LL, LH, HL, HH), and also computed high frequency energy components using LH, HL, and HH. In flame candidates detection, we compute a binary image by thresholding in LL sub-image and apply morphology operations to the binary image to remove noises. After finding initial boundaries, final candidate regions are extracted using expanding initial boundary regions to their neighborhoods. In tracking and flame classification, features of region size and high frequency energy are calculated from candidate regions and tracked using queues, and we classify whether the tracked regions are flames by temporal changes of moving averages.

• Journal of Satellite, Information and Communications
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• v.8 no.4
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• pp.100-104
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• 2013

In this paper, we present an optimal threshold setting method for flame detection of infrared thermal image. Conventional infrared flame detection methods used fixed intensity threshold to segment candidate flame regions and further processing is performed to decide correct flame detection. So flame region segmentation step using the threshold is important processing for fire detection algorithm. The threshold should be change in input image depends on camera types and operation conditions. We have analyzed the conventional thresholds composed of fixed-intensity, average, standard deviation, maximum value. Finally, we extracted that the optimal threshold value is more than summation of average and standard deviation, and less than maximum value. it will be enhance flame detection rate than conventional fixed-threshold method.

• Journal of Korea Multimedia Society
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• v.12 no.11
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• pp.1547-1556
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• 2009

In this paper, we propose a flame detection method using region expansions and on-line variances in outdoor infrared video sequences. To segment flame candidates' regions in infrared images, we first, extract initial regions by high threshold values in infrared images and then the segmented regions are expanded to their neighbors with similar high intensity values. The segmented regions could be non-flame areas like bare-grounds and buildings. Therefore, to detect flame regions in the segmented regions, the segmented regions which have high intensity values in infrared image, are tracked using bounding regions in frame sequences. Variances in the tracked regions are calculated effectively by on-line updates to measure intensity variations on the tracked regions. Experiments show that the proposed method, which is based on region expansions and the average of on-line variances in the regions, is efficient to detect flames in infrared image.

• Fire Science and Engineering
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• v.22 no.4
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• pp.27-32
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• 2008

In this paper, a flame and smoke detection algorithm is proposed to recognize a fire. Flame and smoke have specific color distribution and continuously change shapes of them. In the proposed flame detection algorithm, specific regions are candidated as flame by color distributions and variations of frames of video. Some of candidated regions are decided as flame by the magnitude of motion vector. To determine smoke in the field of view of camera, edge is important because high frequency component is decreased by it. Candidated region of smoke is assigned by color distributions, inter-frame differences and the value of edge. The candidated region is settled as smoke region with magnitude of motion vector. As results of simulations, it is shown that the proposed algorithm is useful for flame and smoke detection.