Fire is a fierce disaster, and smoke is the early signal of fire. Since such features as chrominance, texture, and shape of smoke are very special, a lot of methods based on these features have been developed. But these static characteristics vary widely, so there are some exceptions leading to low detection accuracy. On the other side, the motion of smoke is much more discriminating than the aforementioned features, so a time-domain neural network is proposed to extract its dynamic characteristics. This smoke recognition network has these advantages:(1) extract the spatiotemporal with the 3D filters which work on dynamic and static characteristics synchronously; (2) high accuracy, 87.31% samples being classified rightly, which is the state of the art even in a chaotic environments, and the fuzzy objects for other methods, such as haze, fog, and climbing cars, are distinguished distinctly; (3) high sensitiveness, smoke being detected averagely at the 23rd frame, which is also the state of the art, which is meaningful to alarm early fire as soon as possible; and (4) it is not been based on any hypothesis, which guarantee the method compatible. Finally, a new metric, the difference between the first frame in which smoke is detected and the first frame in which smoke happens, is proposed to compare the algorithms sensitivity in videos. The experiments confirm that the dynamic characteristics are more discriminating than the aforementioned static characteristics, and smoke recognition network is a good tool to extract compound feature.

中文翻译：

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基于动态特性的烟雾识别网络

火灾是凶猛的灾难，烟雾是火灾的早期信号。由于烟雾的色度、纹理、形状等特征非常特殊，因此基于这些特征开发了很多方法。但是这些静态特性差异很大，因此存在一些导致检测精度低的例外情况。另一方面，烟雾的运动比上述特征更具辨别力，因此提出了时域神经网络来提取其动态特征。该烟雾识别网络具有以下优点：（1）利用同步处理动静态特征的3D滤波器提取时空；(2) 准确率高，87.31%的样本被正确分类，即使在混乱的环境中也是最先进的，而其他方法的模糊对象，如雾霾，雾，和爬山车，区别明显；（3）灵敏度高，平均在第23帧检测到烟雾，这也是最先进的，对尽早报警早期火灾有意义；(4) 不基于任何假设，保证方法兼容。最后，提出了一个新指标，即检测到烟雾的第一帧与发生烟雾的第一帧之间的差异，以比较视频中的算法灵敏度。实验证实，动态特征比上述静态特征更具辨别力，烟雾识别网络是提取复合特征的好工具。有利于尽早报警早期火灾；(4) 不基于任何假设，保证方法兼容。最后，提出了一个新指标，即检测到烟雾的第一帧与发生烟雾的第一帧之间的差异，以比较视频中的算法灵敏度。实验证实，动态特征比上述静态特征更具辨别力，烟雾识别网络是提取复合特征的好工具。有利于尽早报警早期火灾；(4) 不基于任何假设，保证方法兼容。最后，提出了一个新指标，即检测到烟雾的第一帧与发生烟雾的第一帧之间的差异，以比较视频中的算法灵敏度。实验证实，动态特征比上述静态特征更具辨别力，烟雾识别网络是提取复合特征的好工具。建议比较视频中的算法灵敏度。实验证实，动态特征比上述静态特征更具辨别力，烟雾识别网络是提取复合特征的好工具。建议比较视频中的算法灵敏度。实验证实，动态特征比上述静态特征更具辨别力，烟雾识别网络是提取复合特征的好工具。