Multi-sensor Fire Detection by Fusing Visual and Non-visual Flame Features

Verstockt, Steven; Vanoosthuyse, Alexander; Hoecke, Sofie Van; Lambert, Peter; Walle, Rik Van de

doi:10.1007/978-3-642-13681-8_39

Cited by 45 publications

(21 citation statements)

References 4 publications

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“…8. In order to objectively evaluate the detection results of different methods, the 'detection rate' metric [61,2] is used which is comparable to the evaluation methods used by Celik et al [60] and Toreyin et al [13].…”

Section: Evaluation Of Visible Range Video Fire Detection Methodsmentioning

confidence: 99%

Video fire detection – Review

Çetin

Dimitropoulos

Gouverneur

et al. 2013

Digital Signal Processing

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231

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This is a review article describing the recent developments in Video based Fire Detection (VFD). Video surveillance cameras and computer vision methods are widely used in many security applications. It is also possible to use security cameras and special purpose infrared surveillance cameras for fire detection. This requires intelligent video processing techniques for detection and analysis of uncontrolled fire behavior. VFD may help reduce the detection time compared to the currently available sensors in both indoors and outdoors because cameras can monitor "volumes" and do not have transport delay that the traditional "point" sensors suffer from. It is possible to cover an area of 100 km2 using a single pan-tilt-zoom camera placed on a hilltop for wildfire detection. Another benefit of the VFD systems is that they can provide crucial information about the size and growth of the fire, direction of smoke propagation.

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Section: Evaluation Of Visible Range Video Fire Detection Methodsmentioning

confidence: 99%

Video fire detection – Review

Çetin

Dimitropoulos

Gouverneur

et al. 2013

Digital Signal Processing

Self Cite

231

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“…Those techniques have already proven to give decent results in general classification and segmentation tasks [45]. Furthermore, the possibilities of combining thermal and visual images for the understanding and fire localization [46] task will be further explored. Besides the detection their are also measurements possible to link the actual fire size or smoke height to fire forecasting modules [21].…”

Section: Smoke and Fire Classificationmentioning

confidence: 99%

Fireground location understanding by semantic linking of visual objects and building information models

Vandecasteele

Merci

Verstockt

2017

Fire Safety Journal

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This paper presents an outline for improved localization and situational awareness in fire emergency situations based on semantic technology and computer vision techniques. The novelty of our methodology lies in the semantic linking of video object recognition results from visual and thermal cameras with Building Information Models (BIM). The current limitations and possibilities of certain building information streams in the context of fire safety or fire incident management are addressed in this paper. Furthermore, our data management tools match higher-level semantic metadata descriptors of BIM and deep-learning based visual object recognition and classification networks. Based on these matches, estimations can be generated of camera, objects and event positions in the BIM model, transforming it from a static source of information into a rich, dynamic data provider. Previous work has already investigated the possibilities to link BIM and lowcost point sensors for fireground understanding, but these approaches did not take into account the benefits of video analysis and recent developments in semantics and feature learning research. Finally, the strengths of the proposed approach compared to the state-of-the-art is its (semi-)automatic workflow, generic and modular setup and multi-modal strategy, which allows to automatically create situational awareness, to improve localization and to facilitate the overall fire understanding.

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“…[1][2][3] However, these methods have problems, namely, it is impossible to tell whether a fire is occurring until smoke or flame spreads to the detection range of sensors and detection takes a long time because a fire alarm is only issued after the flame's influence exceeds a reference temperature or a set value. In addition, because the detection range is restricted to the local position where sensors are installed, the need for numerous distributed sensors and installation increases the cost.…”

Section: Introductionmentioning

confidence: 99%

Fire detection system using random forest classification for image sequences of complex background

Kim¹,

Kang²

2013

Opt. Eng

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Abstract. We present a fire alarm system based on image processing that detects fire accidents in various environments. To reduce false alarms that frequently appeared in earlier systems, we combined image features including color, motion, and blinking information. We specifically define the color conditions of fires in hue, saturation and value, and RGB color space. Fire features are represented as intensity variation, color mean and variance, motion, and image differences. Moreover, blinking fire features are modeled by using crossing patches. We propose an algorithm that classifies patches into fire or nonfire areas by using random forest supervised learning. We design an embedded surveillance device made with acrylonitrile butadiene styrene housing for stable fire detection in outdoor environments. The experimental results show that our algorithm works robustly in complex environments and is able to detect fires in real time.

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Multi-sensor Fire Detection by Fusing Visual and Non-visual Flame Features

Cited by 45 publications

References 4 publications

Video fire detection – Review

Video fire detection – Review

Fireground location understanding by semantic linking of visual objects and building information models

Fire detection system using random forest classification for image sequences of complex background

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