Wavelet-based smoke detection in outdoor video sequences

Gonzalez-Gonzalez, R.; Alarcón-Aquino, Vicente; Rosas-Romero, Roberto; Starostenko, Oleg; Rodrı́guez-Asomoza, J.; Ramírez-Cortés, Juan Manuel

doi:10.1109/mwscas.2010.5548865

Cited by 22 publications

(7 citation statements)

References 4 publications

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“…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. The detection of a candidate smoke region greatly affects the detection rate and final detection efficiency of subsequent procedures.…”

Section: Introductionmentioning

confidence: 99%

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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Section: Introductionmentioning

confidence: 99%

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

et al. 2015

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“…As it is well-known, flames flicker in uncontrolled fires, therefore flicker detection [24,18,12,13,27,28,30] in video and wavelet-domain signal energy analysis [21,14,20,26,31,39] can be used to distinguish ordinary objects from fire. These methods focus on the temporal behavior of flames and smoke.…”

Section: Motion and Flicker Analysis Using Fourier And Wavelet Transfmentioning

confidence: 99%

“…Related to the disorder analysis is the growing of smoke and flame regions in the early stage of a fire. In [31,34], the growth rate of the region-of-interest is used as a feature parameter for fire detection. Compared to disorder metrics, however, growth analysis is less effective in detecting the smoke especially in wildfire detection.…”

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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“…In order to study the infrared extinction characteristics of the smoke screen, infrared sensors are widely applied in practice so as to observe and record the motion of the smoke, which can simulate the battlefield scene [15]. Previous studies have developed a considerable number of methods to detect the smoke screen, such as wavelets, support vector machines, image fusion techniques, color features, and motion detection [16,17,18,19,20]. In addition, Yang et al [21] proposed a test method of the geometric smoke area, which is based on the number of image pixels.…”

Section: Introductionmentioning

confidence: 99%

Infrared Thermography Approach for Effective Shielding Area of Field Smoke Based on Background Subtraction and Transmittance Interpolation

Tang

Zhang

Chen

et al. 2018

Sensors

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Effective shielding area is a crucial indicator for the evaluation of the infrared smoke-obscuring effectiveness on the battlefield. The conventional methods for assessing the shielding area of the smoke screen are time-consuming and labor intensive, in addition to lacking precision. Therefore, an efficient and convincing technique for testing the effective shielding area of the smoke screen has great potential benefits in the smoke screen applications in the field trial. In this study, a thermal infrared sensor with a mid-wavelength infrared (MWIR) range of 3 to 5 μm was first used to capture the target scene images through clear as well as obscuring smoke, at regular intervals. The background subtraction in motion detection was then applied to obtain the contour of the smoke cloud at each frame. The smoke transmittance at each pixel within the smoke contour was interpolated based on the data that was collected from the image. Finally, the smoke effective shielding area was calculated, based on the accumulation of the effective shielding pixel points. One advantage of this approach is that it utilizes only one thermal infrared sensor without any other additional equipment in the field trial, which significantly contributes to the efficiency and its convenience. Experiments have been carried out to demonstrate that this approach can determine the effective shielding area of the field infrared smoke both practically and efficiently.

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Wavelet-based smoke detection in outdoor video sequences

Cited by 22 publications

References 4 publications

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

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

Video fire detection – Review

Infrared Thermography Approach for Effective Shielding Area of Field Smoke Based on Background Subtraction and Transmittance Interpolation

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