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首页> 外文会议>International symposium on multispectral image processing and pattern recognition >Infrared video based gas leak detection method using modified FAST features
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Infrared video based gas leak detection method using modified FAST features

机译：使用改进的FAST功能的基于红外视频的气体泄漏检测方法

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In order to detect the invisible leaking gas that is usually dangerous and easily leads to fire or explosion in time, many new technologies have arisen in the recent years, among which the infrared video based gas leak detection is widely recognized as a viable tool. However, all the moving regions of a video frame can be detected as leaking gas regions by the existing infrared video based gas leak detection methods, without discriminating the property of each detected region, e.g., a walking person in a video frame may be also detected as gas by the current gas leak detection methods.To solve this problem, we propose a novel infrared video based gas leak detection method in this paper, which is able to effectively suppress strong motion disturbances.Firstly, the Gaussian mixture model(GMM) is used to establish the background model.Then due to the observation that the shapes of gas regions are different from most rigid moving objects, we modify the Features From Accelerated Segment Test( FAST) algorithm and use the modified FAST (mFAST) features to describe each connected component. In view of the fact that the statistical property of the mFAST features extracted from gas regions is different from that of other motion regions, we propose the Pixel-Per-Points (PPP) condition to further select candidate connected components.Experimental results show that the algorithm is able to effectively suppress most strong motion disturbances and achieve real-time leaking gas detection.
机译：为了检测通常是危险的并且容易及时引起火灾或爆炸的不可见泄漏气体，近年来出现了许多新技术，其中基于红外视频的气体泄漏检测被广泛认为是可行的工具。然而，通过现有的基于红外视频的气体泄漏检测方法，可以将视频帧的所有运动区域检测为泄漏气体区域，而无需区分每个检测到的区域的属性，例如，还可以检测到视频帧中的步行者为解决这一问题，本文提出了一种新的基于红外视频的气体泄漏检测方法，该方法能够有效地抑制强运动干扰。用于建立背景模型。然后，由于观察到气体区域的形状与大多数刚性移动物体不同，我们修改了“加速分段试验”（FAST）算法的特征，并使用修改后的“ FAST（mFAST）”特征来描述每个连接的组件。鉴于从气体区域提取的mFAST特征的统计特性与其他运动区域的统计特性不同，我们提出了像素每点（PPP）条件，以进一步选择候选连通分量。实验结果表明，该算法能够有效地抑制最强烈的运动干扰并实现实时泄漏气体检测。

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