Wildfire smoke detection using spatiotemporal bag-of-features of smoke

Park, Jun-Oh; Ko, Byoung Chul; Nam, Jae-Yeal; Kwak, Sooyeong

doi:10.1109/wacv.2013.6475019

Cited by 33 publications

(11 citation statements)

References 8 publications

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“…(11) and (12), it follows that P ðH 1 jf ðx; yÞÞ ¼ P ðf ðx; yÞjH 1 ÞP ðH 1 Þ P ðf ðx; yÞjH 0 ÞP ðH 0 Þ þ P ðf ðx; yÞjH 1 ÞP ðH 1 Þ ; and…”

Section: Detection Of a Salient Smoke Regionmentioning

confidence: 97%

“…In addition to having merits similar to those of VFD, VSD provides an alarm much earlier than VFD. Thus, the main emphasis of research has shifted to VSD [7][8][9][10][11]. Most VSD schemes have three stages: [12][13][14][15][16]; the first stage is the detection of a candidate smoke region, the second stage is the extraction and analysis of smoke features, and the final stage is verification of the smoke region.…”

Section: Introductionmentioning

confidence: 99%

“…Beji et al [20] proposed a novel blockwise normalization and gradient analysis method with which to find the edge of the smoke layer, and then calculated the height of the smoke layer. Block processing was also used in other studies [7,10,11,21]. All the methods mentioned above are used to extract smoke regions from the background to lay the foundation for smoke feature analysis and recognition.…”

Section: Introductionmentioning

confidence: 99%

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A Saliency-Based Method for Early Smoke Detection in Video Sequences

et al. 2015

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Video-based smoke detection requires suspected smoke regions to be segmented from the complex background in the initial stage of detection. This segmentation is also important to the subsequent processes of detection. This paper proposes a novel method of segmenting a smoke region in smoke pixel classification based on saliency detection. A salient smoke detection model based on color and motion features is used. First, smoke regions are identified by enhancing the smoke color nonlinearly. The enhanced map and motion map are then used to measure saliency. Finally, the motion energy and saliency map are used to estimate the suspected smoke regions. The estimation result is regarded as our final smoke pixel segmentation result. The performance of the proposed algorithm is verified on a set of videos containing smoke. In the experiments, the method achieves average smoke segmentation precision of 93.0%, and the precision is as high as 99.0% for forest fires. The results are compared with those of three other methods used in the literature, revealing the proposed method to have both a better segmentation result and better precision. We also present encouraging results of smoke segmentation in video sequences obtained using the proposed saliency detection method. Furthermore, the proposed smoke segmentation method can be used for real-time fire detection in color video sequences.

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“…(11) and (12), it follows that P ðH 1 jf ðx; yÞÞ ¼ P ðf ðx; yÞjH 1 ÞP ðH 1 Þ P ðf ðx; yÞjH 0 ÞP ðH 0 Þ þ P ðf ðx; yÞjH 1 ÞP ðH 1 Þ ; and…”

Section: Detection Of a Salient Smoke Regionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Saliency-Based Method for Early Smoke Detection in Video Sequences

et al. 2015

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“…Video smoke detection is difficult over a wide area at a distance due to the slow apparent movement of the smoke at a distance, variations in smoke properties, inconsistent illumination and image quality [52]. Automated smoke detection systems for forest fires have generally relied on a combination of several features associated with the smoke including colour, spatio-temporal correlation, and slow, rising motion [39,40,52,53,54]. Automated video algorithms and systems have been proposed and tested for tower based fire spotting.…”

Section: Fire Detection and Monitoring By Human Observers Or With mentioning

confidence: 99%

Airborne Optical and Thermal Remote Sensing for Wildfire Detection and Monitoring

Allison

Johnston

Craig

et al. 2016

Sensors

216

109

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For decades detection and monitoring of forest and other wildland fires has relied heavily on aircraft (and satellites). Technical advances and improved affordability of both sensors and sensor platforms promise to revolutionize the way aircraft detect, monitor and help suppress wildfires. Sensor systems like hyperspectral cameras, image intensifiers and thermal cameras that have previously been limited in use due to cost or technology considerations are now becoming widely available and affordable. Similarly, new airborne sensor platforms, particularly small, unmanned aircraft or drones, are enabling new applications for airborne fire sensing. In this review we outline the state of the art in direct, semi-automated and automated fire detection from both manned and unmanned aerial platforms. We discuss the operational constraints and opportunities provided by these sensor systems including a discussion of the objective evaluation of these systems in a realistic context.

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“…Obtained features are used as inputs for computational intelligence and statistical classifiers. Park et al (Park et al, 2013) proposed an algorithm for wildfire smoke detection using spatialtemporal bag-of-features (BOF) technique. Slow moving regions are extracted by key-frame detection, followed by rejection of non-smoke colored blocks based on probability density function of smoke color model learned from the training data.…”

Section: Improvements In Algorithms For Wildfire Smoke and Fire Detecmentioning

confidence: 99%

New generation of automatic ground based wildfire surveillance systems

Stipaničev¹,

Bugarić²,

Krstinić³

et al. 2014

Advances in Forest Fire Research

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A navegação consulta e descarregamento dos títulos inseridos nas Bibliotecas Digitais UC Digitalis, UC Pombalina e UC Impactum, pressupõem a aceitação plena e sem reservas dos Termos e Condições de Uso destas Bibliotecas Digitais, disponíveis em https://digitalis.uc.pt/pt-pt/termos.Conforme exposto nos referidos Termos e Condições de Uso, o descarregamento de títulos de acesso restrito requer uma licença válida de autorização devendo o utilizador aceder ao(s) documento(s) a partir de um endereço de IP da instituição detentora da supramencionada licença.Ao utilizador é apenas permitido o descarregamento para uso pessoal, pelo que o emprego do(s) título(s) descarregado(s) para outro fim, designadamente comercial, carece de autorização do respetivo autor ou editor da obra. Na medida em que todas as obras da UC Digitalis se encontram protegidas pelo Código do Direito de Autor e Direitos Conexos e demais legislação aplicável, toda a cópia, parcial ou total, deste documento, nos casos em que é legalmente admitida, deverá conter ou fazer-se acompanhar por este aviso. AbstractThe paper presents a state of the art in the research area of ground based wildfire surveillance systems with special emphasis on their latest generations proposed and developed in the last couple of years. We have focused primarily on novel, more efficient algorithms for wildfire detection, but other topics like fusion of video based systems with other wildfire detection systems and their integration with Geographic Information Systems (GIS) systems has also been analyzed. Our particular focus was on research carried out in our Department. Last but not least, we give our opinions and assumptions about future development of such systems.

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Wildfire smoke detection using spatiotemporal bag-of-features of smoke

Cited by 33 publications

References 8 publications

A Saliency-Based Method for Early Smoke Detection in Video Sequences

A Saliency-Based Method for Early Smoke Detection in Video Sequences

Airborne Optical and Thermal Remote Sensing for Wildfire Detection and Monitoring

New generation of automatic ground based wildfire surveillance systems

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