Transmission: A New Feature for Computer Vision Based Smoke Detection

Long, Chengjiang; Zhao, Jianhui; Han, Shizhong; Xiong, Lu; Yuan, Zhiyong; Huang, Jing; Gao, Weiwei

doi:10.1007/978-3-642-16530-6_46

Cited by 23 publications

(11 citation statements)

References 12 publications

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“…The haze-free images always contain some pixels that have very low intensities in at least one color channel except the region of sky. Some researchers have proposed many easy methods such as [12] [13] to restore the single haze image based on Dark channel prior. In this paper, using the approach based on dark channel prior, we extract the thin smoke region in the early time of the fire.…”

Section: Dark Channel Priormentioning

confidence: 99%

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Video smoke detection based on semitransparent properties

Yuan

Feng-long

2015

The 27th Chinese Control and Decision Conference (2015 CCDC)

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Video smoke detection has been widely researched for its advantages on fire alarm. But false alarm is still an outstanding issue. In this paper, a novel semitransparent properties based on algorithm of video smoke detection is proposed. The original is that the marginal area of smoke is semitransparent. Firstly, in order to get semitransparent region, the background image is matched with the current image in the video which is restored using the haze image optical model.Then, region of interest (ROI) is found by a region growing method. Finally, fuzzy clustering analysis of boundary in ROI is counted to reduce false alarms. The experimental results indicate that the proposed approach can work effectively with non-false alarm.

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Section: Dark Channel Priormentioning

confidence: 99%

“…In [12], a method is proposed to obtain I b and A. With I b that is the minimum pixel in RGB color space and A that is the maximum intensity except the sky, we can estimate t(x) using Equation (2).…”

Section: Dark Channel Priormentioning

confidence: 99%

Video smoke detection based on semitransparent properties

Yuan

Feng-long

2015

The 27th Chinese Control and Decision Conference (2015 CCDC)

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“…Local binary pattern has been used to capture texture features and was applied to smoke detection [7]. The fractal [8] and transmission [9] property of smoke have been employed to detect smoke, which characterize the nature of smoke. In a representative work [10], motion, surface roughness and area randomness information of smoke were all included in the feature vector for smoke detection.…”

Section: Introductionmentioning

confidence: 99%

Video smoke detection algorithm using dark channel priori

Miao

Chen

Wang³

2014

Proceedings of the 33rd Chinese Control Conference

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This paper proposes a dark channel priori based approach for early stage smoke detection in video sequence. Firstly, smoke exhibit less chrominance components and chromaticity analysis is employed to detect grayish pixels in video sequences. Secondly, the motion history image(MHI) method is proposed to capture the motion characteristics of smoke. This moving history representation can be used to determine the current movement of the smoke and to segment the motions induced by the smoke in a video scene. Finally, the intensity of the dark channel is a rough approximation of the thickness of the smoke. It can separate the smoke component from the background, and is used to validate the candidate smoke region. Experiments show that this method can achieve desired smoke region in various scenes with high smoke detection rate.

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“…In the last decade, there have been several significant improvements in video smoke detection [1][2][3][4][5][6][7][8][9] which mostly use special frequency range of smoke by extracting image features such as motion, edge blurring, flickering, and growing contours [3, 7 and 8]. Temporal and spatial wavelet transformation is commonly used in various scenarios to detect smoke [4,9].…”

Section: Introductionmentioning

confidence: 99%

“…Temporal and spatial wavelet transformation is commonly used in various scenarios to detect smoke [4,9]. Energy lowering of smoke in high frequency analysis is also used by several researchers [5,6]. Noise factor in videos makes robust detection difficult when frequency information is used since frequency patterns similar to smoke are highly available in most of the videos [1,3].…”

Section: Introductionmentioning

confidence: 99%

Patch-wise periodical correlation analysis of histograms for real-time video smoke detection

Ince¹,

Kim²,

Lee³

et al. 2014

2014 IEEE International Conference on Industrial Technology (ICIT)

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In this paper, an approach for video smoke detection is proposed. The basic idea is smoke has a highly varying chrominance/luminance texture in long periods. Since smoke has no shape, it also creates high shape changes in long periods. In this paper, two kinds of histogram are employed to observe change in luminance/chrominance texture and shape. Linearly interpolated chrominance/luminance subtraction image is used as input image for periodical analysis after thresholding. Intensity histogram which consists of 256 bins and oriented gradients histogram with 8 bins are employed for this purpose. Smoke generally creates transparent textures in which histogram bins create high variations. By considering the algorithmic cost and nature of smoke, periodical normalized cross-correlation analysis is performed in histogram bins instead of two-dimensional image context which makes algorithm more speedy and efficient for smoke detection. Experiments with a large number of smoke and non-smoke video sequences give promising results.

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Transmission: A New Feature for Computer Vision Based Smoke Detection

Cited by 23 publications

References 12 publications

Video smoke detection based on semitransparent properties

Video smoke detection based on semitransparent properties

Video smoke detection algorithm using dark channel priori

Patch-wise periodical correlation analysis of histograms for real-time video smoke detection

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