Fire is one of the most dangerous natural/manmade disasters that endangers human life and property. Although early fire smoke detection systems have become increasingly widespread, it is particularly important to study videos of these systems to better understand the effectiveness of fire smoke detection, primarily because this will help to reduce losses from fires. Some of these systems have algorithms that tend to regard motion (such as moving people, cars and other non-smoke objects) in surveillance videos as early fire smoke, and this causes them to create false positive alarms. In attempting to resolve the problem of false positives, this paper outlines a new fast detection method that can be applied to early fire smoke. The proposed algorithm relies on color and diffusion characteristics of smoke and also counts the number of pixels in each candidate smoke region. A time window of 30 consecutive frames is defined to fit the linear change rate of smoke every 10 frames. The smoke discrimination criterion is provided through the smoke slope relationship, and a fire alarm is triggered. The algorithm tests five different kinds of scenarios and compares the detected smoke start frame against the observed smoke start frame. In drawing a comparison with the two test algorithms in this paper, this paper extracts experimental results that show the proposed algorithm has better detection speed and accuracy, and also suggests that it can effectively resolve the problem of false positives caused by moving non-smoke elements such as pedestrians and cars.

中文翻译：

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在视频图像直方图中使用斜率拟合的快速早期火灾烟雾检测系统

火灾是危害人类生命和财产的最危险的自然/人为灾害之一。尽管早期火灾烟雾探测系统已变得越来越普遍，但研究这些系统的视频以更好地了解火灾烟雾探测的有效性尤为重要，主要是因为这将有助于减少火灾损失。其中一些系统的算法倾向于将监控视频中的运动（例如移动的人、汽车和其他非烟雾物体）视为早期火灾烟雾，这会导致它们产生误报。为了解决误报问题，本文概述了一种新的快速检测方法，可以应用于早期火灾烟雾。所提出的算法依赖于烟雾的颜色和扩散特性，并计算每个候选烟雾区域中的像素数。定义了 30 个连续帧的时间窗口，以拟合每 10 帧烟雾的线性变化率。通过烟雾斜率关系提供烟雾判别标准，触发火警。该算法测试了五种不同的场景，并将检测到的烟雾开始帧与观察到的烟雾开始帧进行比较。在与本文两种测试算法的对比中，本文提取了实验结果，表明该算法具有更好的检测速度和准确度，也表明该算法能够有效解决非烟雾元素移动引起的误报问题。比如行人和汽车。定义了 30 个连续帧的时间窗口，以拟合每 10 帧烟雾的线性变化率。通过烟雾斜率关系提供烟雾判别标准，触发火警。该算法测试了五种不同的场景，并将检测到的烟雾开始帧与观察到的烟雾开始帧进行比较。在与本文两种测试算法的对比中，本文提取了实验结果，表明该算法具有更好的检测速度和准确度，也表明该算法可以有效解决移动非烟雾元素引起的误报问题。比如行人和汽车。定义了 30 个连续帧的时间窗口，以拟合每 10 帧烟雾的线性变化率。通过烟雾斜率关系提供烟雾判别标准，触发火警。该算法测试了五种不同的场景，并将检测到的烟雾开始帧与观察到的烟雾开始帧进行比较。在与本文两种测试算法的对比中，本文提取了实验结果，表明该算法具有更好的检测速度和准确度，也表明该算法可以有效解决移动非烟雾元素引起的误报问题。比如行人和汽车。通过烟雾斜率关系提供烟雾判别标准，触发火警。该算法测试了五种不同的场景，并将检测到的烟雾开始帧与观察到的烟雾开始帧进行比较。在与本文两种测试算法的对比中，本文提取了实验结果，表明该算法具有更好的检测速度和准确度，也表明该算法能够有效解决非烟雾元素移动引起的误报问题。比如行人和汽车。通过烟雾斜率关系提供烟雾判别标准，触发火警。该算法测试了五种不同的场景，并将检测到的烟雾开始帧与观察到的烟雾开始帧进行比较。在与本文两种测试算法的对比中，本文提取了实验结果，表明该算法具有更好的检测速度和准确度，也表明该算法能够有效解决非烟雾元素移动引起的误报问题。比如行人和汽车。