Emergency incidents and events of fires can be dangerous and required quick and accurate decision-making need quick and correct decision-making. The use of computer vision for fire detection can provide a efficient solution to deal with these situations. These systems handle the usual data, provide an automated solution, and discard non-relevant information without discarding relevant content. Researchers developed many techniques for fire detection in videos and still images by using different color-based models. However, for videos, these methods are unsuitable because of high false-positive results. These methods use few parameters with little physical meaning, which makes fire detection more difficult. To deal with this, we have proposed a novel fire detection method based on Red Green Blue and CIE $$L * a * b$$ color models, by combining motion detection with tracking fire objects. We have eliminated the moving region and calculate the growth rate of the fire to reduce false-alarm and calculate the risk. The proposed method operates on a reduced number of parameters compared to the existing methods. Experimental results demonstrate the effectiveness of our method of reducing false positives while keeping their precision compatible with the existing methods.

中文翻译：

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使用多色空间和背景建模进行火灾检测

紧急事件和火灾事件可能是危险的，需要快速准确的决策需要快速正确的决策。使用计算机视觉进行火灾探测可以提供一种有效的解决方案来处理这些情况。这些系统处理常用数据，提供自动化解决方案，并在不丢弃相关内容的情况下丢弃不相关的信息。研究人员通过使用不同的基于颜色的模型开发了许多用于视频和静止图像火灾检测的技术。然而，对于视频，这些方法不适合，因为假阳性结果很高。这些方法使用的参数很少，物理意义不大，这使得火灾检测更加困难。为了解决这个问题，我们提出了一种基于红绿蓝和 CIE $$L * a * b$$ 颜色模型的新型火灾检测方法，通过将运动检测与跟踪火对象相结合。我们已经消除了移动区域并计算了火灾的增长率，以减少误报并计算风险。与现有方法相比，所提出的方法对减少的参数数量进行操作。实验结果证明了我们的方法在减少误报的同时保持其精度与现有方法兼容的有效性。