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A hybrid fire detection using Hidden Markov Model and luminance map

机译:使用隐马尔可夫模型和亮度图的混合火灾探测

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Computer vision and pattern recognition is hot research topic recently, fire and flame recognition is an important sub-topics. Although researchers proposed many methods, there exist high false alarm and complex environment inadaptation because of fire-colored moving objects interference. This paper proposed a hybrid method using Hidden Markov Model (HMM) based on spatio-temporal feature and the variance of temporal luminance to detect fire. Here, the HMM model based spatio-temporal feature characterize the flicker feature of fire, and a series of observation points will be set on the boundary of flame to judge the fire flicker feature. While the variance of luminance character the temporal luminance feature. First, we can get the “candidate fire region”, then we concentrate our main work on analyze the “candidate fire region”. Experiment results show our method has a good result and it is robust to be used in complex environment compared with previous algorithms.
机译:计算机视觉和模式识别是近来研究的热点,火灾和火焰识别是一个重要的子主题。尽管研究人员提出了许多方法,但是由于火色运动物体的干扰,仍然存在较高的虚警率和复杂的环境适应性。提出了一种基于隐马尔可夫模型(HMM)的混合方法,该方法基于时空特征和时间亮度的变化来检测火灾。在这里,基于HMM模型的时空特征表征了火的闪烁特征,并且将在火焰的边界上设置一系列观察点来判断火的闪烁特征。而亮度的变化特征是暂时的亮度特征。首先,我们可以得到“候选火区”,然后将主要工作集中在分析“候选火区”上。实验结果表明,与以前的算法相比,该方法具有良好的效果,在复杂环境中具有较强的应用价值。

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