Abstract

In order for detection of a fire in fields, it is effective to detect smoke since it often behaves as a precursor of the fire. One preferable way for early detection is to use visual information from outdoor cameras that widely monitor the filed. There have been many attempts to detect smokes via optical sensors on digital cameras using optical flow methods, but not fully successful from practical-use aspects. It is because the area of smokes occupying on the footage by outdoor cameras is not necessarily large enough. Moreover, in case of urban cities, discrimination of the smokes from other moving objects such as cars, trees and turbines is not easy. Herein we propose a novel method to detect daytime smokes based on variance of optical flow and characteristics of HSV (hue-saturation-value) color. We apply the method to a set of footage of three days obtained in an industrial zone in Japan. Successful results are obtained as over 90% of smokes are detected. Notable is that this method is independent of solar radiation conditions on sunny and cloudy days.

中文翻译：

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基于室外摄像机录像光流方差和HSV颜色特征的日间烟雾检测方法

摘要

为了检测现场火灾，检测烟雾是有效的，因为烟雾通常是火灾的前兆。早期检测的一种优选方法是使用广泛监控现场的室外摄像机的视觉信息。人们已经进行了许多尝试，利用光流方法通过数码相机上的光学传感器来检测烟雾，但从实际使用方面来看并没有完全成功。这是因为室外摄像机拍摄的画面中烟雾所占的面积不一定足够大。此外，在城市中，区分来自其他移动物体（例如汽车、树木和涡轮机）的烟雾并不容易。在此，我们提出了一种基于光流变化和 HSV（色调-饱和度-值）颜色特征来检测白天烟雾的新方法。我们将该方法应用于在日本一个工业区获得的一组三天的镜头。检测到超过 90% 的烟雾，取得了成功的结果。值得注意的是，该方法与晴天和阴天的太阳辐射条件无关。