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首页> 外文期刊>Journal of visual communication & image representation >Fire detection using statistical color model in video sequences
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Fire detection using statistical color model in video sequences

机译：在视频序列中使用统计颜色模型进行火灾探测

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摘要

In this paper, we propose a real-time fire-detector that combines foreground object information with color pixel statistics of fire. Simple adaptive background model of the scene is generated by using three Gaussian distributions, where each distribution corresponds to the pixel statistics in the respective color channel. The foreground information is extracted by using adaptive background subtraction algorithm, and then verified by the statistical fire color model to determine whether the detected foreground object is a fire candidate or not. A generic fire color model is constructed by statistical analysis of the sample images containing fire pixels. The first contribution of the paper is the application of real-time adaptive background subtraction method that aids the segmentation of the fire candidate pixels from the background. The second contribution is the use of a generic statistical model for refined fire-pixel classification. The two processes are combined to form the fire detection system and applied for the detection of fire in the consecutive frames of video sequences. The frame-processing rate of the detector is about 40 fps with image size of 176 x 144 pixels, and the algorithm's correct detection rate is 98.89%.
机译：在本文中，我们提出了一种将前景物体信息与火灾的彩色像素统计信息相结合的实时火灾探测器。通过使用三个高斯分布生成场景的简单自适应背景模型，其中每个分布对应于相应颜色通道中的像素统计信息。通过使用自适应背景减除算法提取前景信息，然后通过统计火色模型进行验证，以确定检测到的前景对象是否为候选火灾。通过对包含火灾像素的样本图像进行统计分析，可以构建通用火灾颜色模型。本文的第一个贡献是实时自适应背景减影方法的应用，该方法有助于从背景中分割火灾候选像素。第二个贡献是将通用统计模型用于精细的火象素分类。将这两个过程组合起来以形成火灾检测系统，并将其应用于视频序列连续帧中的火灾检测。该检测器的帧处理速率约为40 fps，图像尺寸为176 x 144像素，该算法的正确检测率为98.89％。

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