Object Discrimination by Infrared Image Processing

Bosch, Ignacio; Gómez, Soledad; Molina, R.

doi:10.1007/978-3-642-02267-8_4

Cited by 21 publications

(14 citation statements)

References 4 publications

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“…by temporal median filtering, it is expected that this number of false alarms can even be further reduced. However, to confirm all these findings a more thorough evaluation and comparison with other work [2][3][4] is needed. This will be part of our future work.…”

Section: Resultsmentioning

confidence: 95%

“…The combination of both temporal and spatial clues seems more appropriate than the luminosity approach and, according to the authors [3], greatly reduces false alarms. A similar combination of temporal and spatial features is used by Bosch et al [4]. Hotspots, i.e.…”

Section: Related Work In Non-visible Lightmentioning

confidence: 99%

“…Like in the work of Bosch et al [4] we extract the hot objects representing possible flames by separating the highly brightened objects from the less brightened objects (Fig. 2).…”

Section: Lwir Hot Object Segmentationmentioning

confidence: 99%

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

Verstockt

Vanoosthuyse

Hoecke

et al. 2010

Lecture Notes in Computer Science

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Abstract. This paper proposes a feature-based multi-sensor fire detector operating on ordinary video and long wave infrared (LWIR) thermal images. The detector automatically extracts hot objects from the thermal images by dynamic background subtraction and histogram-based segmentation. Analogously, moving objects are extracted from the ordinary video by intensity-based dynamic background subtraction. These hot and moving objects are then further analyzed using a set of flame features which focus on the distinctive geometric, temporal and spatial disorder characteristics of flame regions. By combining the probabilities of these fast retrievable visual and thermal features, we are able to detect the fire at an early stage. Experiments with video and LWIR sequences of fire and non-fire real case scenarios show good results and indicate that multi-sensor fire analysis is very promising.

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Section: Resultsmentioning

confidence: 95%

Section: Related Work In Non-visible Lightmentioning

confidence: 99%

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

Verstockt

Vanoosthuyse

Hoecke

et al. 2010

Lecture Notes in Computer Science

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“…When there is no or very little visible light or the color of the object to be detected is similar to the background, IR imaging systems provide solutions [62,63,64,65,66,67,68]. Although there is an increasing trend in IR-camera based intelligent video analysis, the number of papers in the area of IR-based fire detection is few [64,65,66,67,68].…”

Section: Video Fire Detection In Infrared (Ir) Spectral Rangementioning

confidence: 99%

“…Although there is an increasing trend in IR-camera based intelligent video analysis, the number of papers in the area of IR-based fire detection is few [64,65,66,67,68]. This is mainly due to the high cost of IR imaging systems compared to ordinary cameras.…”

Section: Video Fire Detection In Infrared (Ir) Spectral Rangementioning

confidence: 99%

Video fire detection – Review

Çetin

Dimitropoulos

Gouverneur

et al. 2013

Digital Signal Processing

238

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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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Fire and Smoke Image Recognition

Ko,

Hamed Mozaffari,

2024

Digital Innovations in Architecture, Engineering and Construction

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Object Discrimination by Infrared Image Processing

Cited by 21 publications

References 4 publications

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

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

Video fire detection – Review

Fire and Smoke Image Recognition

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