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WAVELET BASED REAL-TIME SMOKE DETECTION IN VIDEO

机译:视频中基于小波的实时烟雾检测

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摘要

A method for smoke detection in video is proposed. It is assumed the camera monitoring the scene is stationary. Since the smoke is semi-transparent, edges of image frames start loosing their sharpness and this leads to a decrease in the high frequency content of the image. To determine the smoke in the field of view of the camera, the background of the scene is estimated and decrease of high frequency energy of the scene is monitored using the spatial wavelet transforms of the current and the background images. Edges of the scene are especially important because they produce local extrema in the wavelet domain. A decrease in values of local extrema is also an indicator of smoke. In addition, scene becomes grayish when there is smoke and this leads to a decrease in chrominance values of pixels. Periodic behavior in smoke boundaries and convexity of smoke regions are also analyzed. All of these clues are combined to reach a final decision.
机译:提出了一种视频烟感检测方法。假定监视场景的摄像机是静止的。由于烟雾是半透明的,因此图像帧的边缘开始失去清晰度,这导致图像的高频含量降低。为了确定照相机视场中的烟雾,估计场景的背景,并使用当前图像和背景图像的空间小波变换来监视场景的高频能量的减少。场景的边缘特别重要,因为它们会在小波域中产生局部极值。局部极值的降低也是烟雾的指示。另外,当有烟雾时,场景变灰,这导致像素的色度值降低。还分析了烟雾边界和烟雾区域凸度的周期性行为。所有这些线索都被结合在一起以做出最终决定。

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