A vision-based system for early fire detection

Santana, Pedro; Gomes, Pedro; Barata, José

doi:10.1109/icsmc.2012.6377815

Cited by 15 publications

(8 citation statements)

References 14 publications

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“…Our new fire warning system is designed to be equipped with a normal web camera detection module that detects fire in real-time based on frequency features of frames, and its main function is to improve the previous work in reducing false fire alarms by distinguishing fire information from other objects present in the scene. The common process of in detection methods consist of two steps: the first step is extracting features; the second is using learning method or classifying method to build classifiers based on those features [14]. This is achieved through frames reprocessing, discrete cosine for features extraction and recognition based on neural networks.…”

Section: Proposed Approachmentioning

confidence: 99%

Discrete Cosine Coefficients as Images features for Fire Detection based on Computer Vision

Dukuzumuremyi¹,

Zou²,

Mukamakuza³

et al. 2014

JCP

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Fire hazards occurring recently in the world lead to the need of designing accurate fire detection systems in order to save human lives. The newest innovations continue to use cameras and computer algorithms to analyze the visible effects of fire and its motion in their applications like the adaboost classifier which is well known for its strength in rigid objects detection from images. This paper presents a Fire Detection System (FDS) with an algorithm that works side by side with the adaboost classifier to determine the presence of fire in an image taken by a normal web camera (webcam), in order to decrease the false alarms in an indoor scene. The images are first preprocessed and their selected discrete cosine coefficients are kept for analysis to get better coefficients that will be fed to a neural network for classification and results are compared to a statistical approach used in combination with binary background mask (BBM) and a wavelet-based model of fire's frequency signature(WMF) to test its accuracy.

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Section: Proposed Approachmentioning

confidence: 99%

Discrete Cosine Coefficients as Images features for Fire Detection based on Computer Vision

Dukuzumuremyi¹,

Zou²,

Mukamakuza³

et al. 2014

JCP

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“…Although MCRD selects candidate regions of fire, the candidate regions may still include moving objects with colors that are similar to the color of fire such as red vehicles, vehicle brake lights, and a person wearing red clothes. To deal with this issue, many researchers then detect more refined regions of fire using color information [2][3][4][5][6][7][9][10][11]14,16,17,20,25,26], and extract features of fire including characteristics of fire such as flicker [10,11,23,26,27], color variations under spatial wavelet analysis [11,23,26], and dynamic textures [19]. Finally, a classifier is employed to determine if there is a fire or a non-fire in a processing movie frame.…”

Section: Introductionmentioning

confidence: 99%

“…Most are based on the multi-stage pattern recognition, which basically consists of the following four stages [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]: movement-containing region detection (MCRD), color segmentation (CS), feature extraction (FE), and classification (CLASSIFY). MCRD is a fundamental task in computer vision-based fire detection and a number of methods have been proposed to detect movement-containing regions from static cameras, including optical flow [12,22], temporal differencing [21], Gaussian mixture modeling [10,17], and background subtraction [11,13,16,20,[23][24][25].…”

Section: Introductionmentioning

confidence: 99%

An optimal many-core model-based supercomputing for accelerating video-equipped fire detection

et al. 2015

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Automatic fire detection has become more and more appealing because of the increasing use of video capabilities in surveillance systems used for early detection of fire. However, its high computational complexities limit its use in real-time applications. To meet the real-time processing of today's fire detection techniques, this study proposes a single instruction, multiple data many-core model. To design an efficient many-core model for image processing applications such as fire detection, a key design parameter is the image data-per-processing-element (IDPE) variation of the many-core system, which is the amount of image data directly mapped to each processing element PE. This study quantitatively evaluates the impact of the IDPE variation on system performance and energy efficiency for the multi-stage fire detection approach that consists of movement-containing region detection, color segmentation, fire feature extraction of fires, and decision making if there is a fire or non-fire in a processing video frame. In this study, we use six IDPE ratios to determine an optimal many-core model that provides the most efficient operation for fire detection using architectural and workload simulation. Experimental results indicate that the most efficient many-core model is achieved at the 64 IDPE value in terms of the worst-case execution time and energy 123 J. Seo et al. efficiency. In addition, this study compares the performance of the most efficient manycore configuration with that of a commercial graphics processing unit (Nvidia GeForce GTX 480) to show the improved performance of the proposed many-core model for the fire detection algorithm. This many-core configuration outperforms the commercial graphic processing unit in the worst-case execution time and energy efficiency.

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“…There have been some studies on vision-based techniques for fire detection, particularly for forest fire detection. Santana et al [3] described a video-based system for fire detection in which techniques such as model-based false alarms rejection, color-based fire's appearance model, wavelet-based model of fire's frequency signature and the camera-world mapping were employed for the identification of the fire from video images. Töreyin et al [4] proposed an image processing technique for fire and flame detection where a wavelet transform was used to determine the color variations in flame regions.…”

Section: Introductionmentioning

confidence: 99%

Fire detection using stereoscopic imaging and image processing techniques

Li¹,

Lü

Yan³

2014

2014 IEEE International Conference on Imaging Systems and Techniques (IST) Proceedings

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2014) Fire detection using stereoscopic imaging and image processing techniques'.Abstract-This paper presents a stereoscopic imaging technique for fire detection. A low-cost binocular CCD camera is used to acquire the stereoscopic images of a controlled gas fire. Computer algorithms are developed to segment the fire zone in the HSV (Hue, Saturation, Value) color space and to determine the correspondence points of image pairs. The location, size and propagation of the fire are then determined from the perspective relationship between the fire and the camera. Experimental results under laboratory conditions show the effectiveness of the proposed approach for fire detection.

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A vision-based system for early fire detection

Cited by 15 publications

References 14 publications

Discrete Cosine Coefficients as Images features for Fire Detection based on Computer Vision

Discrete Cosine Coefficients as Images features for Fire Detection based on Computer Vision

An optimal many-core model-based supercomputing for accelerating video-equipped fire detection

Fire detection using stereoscopic imaging and image processing techniques

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