Onset fire detection in video sequences using region based structure from motion for non-rigid bodies algorithm

Umar, Malik Mohamed; Silva, L.C.B.

doi:10.1049/cp.2018.1533

Cited by 3 publications

(2 citation statements)

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“…The flame detection methods in the video can be roughly divided into three categories, the first is to use flame colour, texture and other features to detect the flame, the second is to use a depth neural network model to detect flame in pictures, and the third is to combine traditional digital image processing methods with deep neural network model to detect flame. Chelik et al used colour features for flame detection [1][2][3][4][5][6][7], but this method had a poor effect on areas with similar colours in the background; In order to improve the accuracy of flame detection, Yan [8][9][10][11][12][13] and others fused the colour, texture and flicker features of flame on the basis of RGB-HSI hybrid model to realize efficient fire detection; Zhao et al [14] proposed to use SVM to judge fire by combining various static and dynamic features, but the analysis of static features was still emphasized, and the extraction of dynamic features was less [15][16][17][18][19]; to overcome the non-rigidity of the flame object, Collumeau et al [20] used SVM to divide the visual scene into flame and non-flame areas, and introduced colour features to In order to improve the accuracy of detection, researchers put forward a new idea, which combined traditional image processing methods with depth neural networks, and achieved better results than a single method. For example, Luo et al [24] proposed a background update strategy considering the initial frame in order to solve the blank hole phenomenon in the process of rough extraction of moving regions by traditional technologies such as GMM and optical flow.…”

Section: Introductionmentioning

confidence: 99%

“…Chelik et al. used colour features for flame detection [1–7], but this method had a poor effect on areas with similar colours in the background; In order to improve the accuracy of flame detection, Yan [8–13] and others fused the colour, texture and flicker features of flame on the basis of RGB‐HSI hybrid model to realize efficient fire detection; Zhao et al. [14] proposed to use SVM to judge fire by combining various static and dynamic features, but the analysis of static features was still emphasized, and the extraction of dynamic features was less [15–19]; to overcome the non‐rigidity of the flame object, Collumeau et al.…”

Section: Introductionmentioning

confidence: 99%

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A hybrid method of detecting flame from video stream

Dou

Xie

et al. 2022

IET Image Processing

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In this paper, a method of detecting flame from video stream is proposed exploiting the characteristics of the disordered movement, rapid deformation and intense colour of the flame. Firstly, the frame difference between video frame and background frame is calculated to obtain the main part of the moving object, and the difference between frames is calculated frame by frame in time series to obtain the deformation part of the moving object, and then the sum of cumulative difference between frames and the background difference between frames are added to generate a binary image containing the moving object and the deformed part. Secondly, the binary image is morphologically opened, and rectangular segmentation is carried out to obtain multiple suspicious flame regions. Finally, in the light of the intense colour of the flame, the corresponding area is extracted from the original picture by using the segmentation rectangle, and the colour statistics of the area are carried out to further judge whether there is a burning flame in the area. The experimental results show that the algorithm can accurately detect the burning area of flame in the real scene and eliminate the light interference and the movement interference.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A hybrid method of detecting flame from video stream

Dou

Xie

et al. 2022

IET Image Processing

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A Rapid Smoke Detection in Real-Time CCTV Videos Using Non-Rigid Bodies Algorithm

Umar

Raj

Silva

et al. 2022

2022 International Conference on Futuristic Technologies (INCOFT)

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A Review of the Applications of Remote Sensing in Fire Ecology

2019

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Wildfire plays an important role in ecosystem dynamics, land management, and global processes. Understanding the dynamics associated with wildfire, such as risks, spatial distribution, and effects is important for developing a clear understanding of its ecological influences. Remote sensing technologies provide a means to study fire ecology at multiple scales using an efficient and quantitative method. This paper provides a broad review of the applications of remote sensing techniques in fire ecology. Remote sensing applications related to fire risk mapping, fuel mapping, active fire detection, burned area estimates, burn severity assessment, and post-fire vegetation recovery monitoring are discussed. Emphasis is given to the roles of multispectral sensors, lidar, and emerging UAS technologies in mapping, analyzing, and monitoring various environmental properties related to fire activity. Examples of current and past research are provided, and future research trends are discussed. In general, remote sensing technologies provide a low-cost, multi-temporal means for conducting local, regional, and global-scale fire ecology research, and current research is rapidly evolving with the introduction of new technologies and techniques which are increasing accuracy and efficiency. Future research is anticipated to continue to build upon emerging technologies, improve current methods, and integrate novel approaches to analysis and classification.

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Onset fire detection in video sequences using region based structure from motion for non-rigid bodies algorithm

Cited by 3 publications

References 0 publications

A hybrid method of detecting flame from video stream

A hybrid method of detecting flame from video stream

A Rapid Smoke Detection in Real-Time CCTV Videos Using Non-Rigid Bodies Algorithm

A Review of the Applications of Remote Sensing in Fire Ecology

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