A system for real-time fire detection

Healey, Glenn; Slater, Don; Lin, Tsung Yi; Drda, B.; Goedeke, A. D.

doi:10.1109/cvpr.1993.341064

Cited by 124 publications

(79 citation statements)

References 1 publication

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“…CCD (charge-coupled device) 카메라를 이용한 영상에서 화재를 인식하고 감지하는 방법으로는 Red Filtering을 이 용한 화재 영상 분할 (1) , 동영상 내에서 화재 위치의 움직임 변화를 이용한 방법 (2)(3)(4) , 영상의 히스토그램의 분포를 이용 한 화재 영역 분할 방법 (5) , 또한 차분영상 데이터를 이용하 는 시간적 변화에 기반한 방법 (6) Figure 1은 RGB 모델이다 (9) . CCD 카메라로 입력되는 RGB(Red, Green, Blue)영상은 컴퓨터 그래픽스에서 사용 되는 3차원 직각좌표계이며, 빛의 삼원색을 혼합하여 나타 날 수 있는 빛의 다양성을 보여주고 있다.…”

Section: 서 론 정보기술(Information Technology: It)의 발전에 의하여 단순한 센서를 활용한 화재unclassified

Smart Fire Image Recognition System using Charge-Coupled Device Camera Image

Kim¹

2013

Journal of Korean Institute of Fire Science and Engineering

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This research suggested smart fire recognition system which trances firing location with CCD camera with wired/wireless TCP/IP function and Pan/Tilt function, delivers information in real time to android system installed by smart mobile communication system and controls fire and disaster remotely. To embody suggested method, firstly, algorithm which applies hue saturation intensity (HSI) Transform for input video, eliminates surrounding lightness and unnecessary videos and segmentalized only firing videos was suggested. Secondly, Pan/Tilt function traces accurate location of firing for proper control of firing. Thirdly, android communication system installed by mobile function confirms firing state and controls it. To confirm the suggested method, 10 firing videos were input and experiment was conducted. As the result, all of 10 videos segmentalized firing sector and traced all of firing locations. , 동영상 내에서 화재 위치의 움직임 변화를 이용한 방법 (2)(3)(4) , 영상의 히스토그램의 분포를 이용 한 화재 영역 분할 방법

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Section: 서 론 정보기술(Information Technology: It)의 발전에 의하여 단순한 센서를 활용한 화재unclassified

Smart Fire Image Recognition System using Charge-Coupled Device Camera Image

Kim¹

2013

Journal of Korean Institute of Fire Science and Engineering

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“…One is to use the color information of the flame and smoke, and the other one uses a dynamic movement [1], [2]. Erroneous detection occurs according to a similar color or lighting color in the surrounding environment.…”

Section: Introductionmentioning

confidence: 99%

Smoke Detection Using GMM and Adaboost

Kim¹,

Ryu²,

Park³

2014

IJCCE

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Abstract-Detection performance of the smoke in the outdoor depends on weather conditions, the shadow by the movement of the sun, or illumination changes, etc. In this paper, a smoke detection system in conjunction with a robust background estimate algorithm to environment change in the outdoor in daytime is proposed. Gaussian Mixture Model (GMM) is applied as background estimation algorithm, and also, Adaboost algorithm is applied to detect the smoke for candidate region. Through the experiments with input videos obtained from a various weather conditions at the same actual road, the proposed algorithms were useful to detect smoke in the outdoor.

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“…Healey et al [6] presented a fire detection system using color video input for a pre-allocated view on some ideal conditions. Noda and Ueda [10] used gray-scale images obtained from cameras to detect fire in tunnels.…”

Section: Introductionmentioning

confidence: 99%

Image-Based Fire Detection Using Neural Networks

Horng¹,

Peng²

2006

Proceedings of the 9th Joint International Conference on Information Sciences (JCIS-06)

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An image-based fire detection method using neural networks is proposed in this paper. First, flame color features, based on the HSI color model, are trained by a backpropagation neural network for flame recognition. Then, based on the learned flame color features, regions with fire-like colors are roughly separated from an image. Besides segmenting flame regions, background objects with similar fire colors or resulted from the reflection of fire flames are also separated from the image. In order to get rid of these spurious fire-like regions, the image difference method and the invented color masking technique are applied. Finally, a compact method is devised to estimate the burning degree of fire flames so that users could be informed with a proper warning alarm. The proposed system can achieve 96.47% fire detection rate on average.

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A system for real-time fire detection

Cited by 124 publications

References 1 publication

Smart Fire Image Recognition System using Charge-Coupled Device Camera Image

Smart Fire Image Recognition System using Charge-Coupled Device Camera Image

Smoke Detection Using GMM and Adaboost

Image-Based Fire Detection Using Neural Networks

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