Dynamic texture detection, segmentation and analysis

Töreyi̇n, Behçet Uğur; Dedeoğlu, Yiğithan; Çetin, A. Enis; Fazekas, Sándor; Chetverikov, Dmitry; Amiaz, T.; Kiryati, Nahum

doi:10.1145/1282280.1282304

Cited by 16 publications

(9 citation statements)

References 7 publications

(6 reference statements)

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“…Currently, a wide variety of methods including geometric, model-based, statistical and motion based techniques are used for dynamic texture detection [48,49,50].…”

Section: Dynamic Texture and Pattern Analysismentioning

confidence: 99%

Video fire detection – Review

Çetin

Dimitropoulos

Gouverneur

et al. 2013

Digital Signal Processing

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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“…Currently, a wide variety of methods including geometric, model-based, statistical and motion based techniques are used for dynamic texture detection [48,49,50].…”

Section: Dynamic Texture and Pattern Analysismentioning

confidence: 99%

Video fire detection – Review

Çetin

Dimitropoulos

Gouverneur

et al. 2013

Digital Signal Processing

231

View full text Add to dashboard Cite

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“…Similarly, Favorskaya and Levtin [3] tracked effectively a smoke propagation by a spatio-temporal clustering of moving regions with a turbulence parameter connecting with fractal properties of smoke. High-frequency analysis of moving pixels was conducted by wavelet transform for smoke flickering analysis and a measure of smoke turbulence as in [4,5]. Tian et al [6] presented a new smoke detection scheme by background modeling where the estimation of the blending parameter and the actual smoke component were formulated as an optimization problem.…”

Section: Introductionmentioning

confidence: 99%

Early smoke detection of forest fire video using CS Adaboost algorithm

Zhao

2015

Optik - International Journal for Light and Electron Optics

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“…Improvements in flame detection algorithms however were achieved in late 2006, using wavelet and image intensity analysis (Toreyin et al, 2007). VBFD with both flame detection and smoke detection capabilities were introduced.…”

Section: Figure 12: Dominant Flame Lookup Table Creation Processmentioning

confidence: 99%

“…References: (Litton, 2009) (Guillemant, 2001), (Kessinger, 2008), (Marbach, Loepfe & Brupbacher, 2006), (Schultze & Willms, 2005), (Toreyin et al, 2007), (Yang, Tseng & Yang, 2008), (Yuan, 2007(Yuan, , 2008). …”

mentioning

confidence: 99%

Improving fire safety and asset loss control in mining: Evaluation and development of a video based fire detection system

Mendham¹

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The overall results of this research addressed the question of how increased time for mineworkers to evacuate mines prior to the onset of untenable fire conditions is achievable. Additionally, improved asset loss control for underground mining is feasible through the successful development of Video Based Fire Detection (VBFD) in mining, as it provides more information earlier for mine fire fighters to intervene in the growth and spread of fire, therefore maintainable coal production is achievable. VBFD may also be used to activate automatic fire suppression systems.VBFD is an emerging volume type smoke detection technology that utilises Closed Circuit Television (CCTV) cameras to capture and analyse real time video data for the purpose of smoke recognition. VBFD technology performance was compared with the performance of fire detection methods currently used in underground coalmines to determine whether valuable improvements to fire safety and asset loss control could be made. Monoxide (CO) concurrent at the VBFD alarm activation time. Sensitivity analysis was undertaken by varying the air velocity over the fire source, which affected the smoke plume shape. The other component of the sensitivity analysis involved variations in light levels aimed at testing the VBFD smoke detection sensitivity. The data were subsequently analysed to identify trends in the detection response of both the CO sensors and the VBFD. Some of the tests carried out exceeded the capability of the VBFD system, which was an important aspect in the research and the evolving capability of video smoke detection.iii The experimental fire characteristics were subsequently replicated numerically using Computational Fluid Dynamics (CFD) so it could be used in this research to estimate the evacuation response of mineworkers under more onerous fire conditions, which could not physically be undertaken in an operational mine.A following VBFD field study was proposed to review and utilise the results of installation commissioning data and maintenance testing of a VBFD system at an operational underground mine environment. The purpose of this field-testing was to inform the underground mining industry of limitations that may exist in terms of the VBFD reliability in service, such as potential maintenance issues. During the period of the subject research the Australian resources industry moved from being in a position to readily facilitate and support minor external unfunded research projects within its underground mine assets, to its current position where it was no longer willing or able to assist. As a result of the lack of a trial VBFD system in an underground mine, the subsequent field study opportunistically considered the reliability and performance of VBFD in a very similar and related environment, however this facility was not an underground mine, but a South African coal fired power station. The results of the reliability testing of 360 VBFD devices over a 12 months' period showed that VBFD was very reliable.Future VBFD research might ad...

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Dynamic texture detection, segmentation and analysis

Cited by 16 publications

References 7 publications

Video fire detection – Review

Video fire detection – Review

Early smoke detection of forest fire video using CS Adaboost algorithm

Improving fire safety and asset loss control in mining: Evaluation and development of a video based fire detection system

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