Spatio-Temporal Flame Modeling and Dynamic Texture Analysis for Automatic Video-Based Fire Detection
Spatio-Temporal Flame Modeling and Dynamic Texture Analysis for Automatic Video-Based Fire Detection
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DOI：
10.1109/tcsvt.2014.2339592
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发表时间：
2015-02-01
影响因子：
8.4
通讯作者：
Grammalidis, Nikos
中科院分区：
文献类型：
--
作者：
Dimitropoulos, Kosmas;Barmpoutis, Panagiotis;Grammalidis, Nikos
每年，世界各地都有大量的野火烧毁林地，造成不利的生态，经济和社会影响。除了采取预防措施外，预警和立即反应是避免重大损失的唯一途径。为此，在本文中，我们提出了一种计算机视觉方法用于火灾火焰检测的预警火灾监控系统。首先，在一个帧中的候选火灾区域定义使用背景减法和颜色分析的基础上的非参数模型。随后，火灾行为建模采用各种时空特征，如颜色概率，闪烁，空间，时空能量，而动态纹理分析应用在每个候选区域使用线性动力系统和袋的系统方法。为了提高算法的鲁棒性，每个候选火灾区域的时空一致性能量估计通过利用先验知识的可能存在的火灾在相邻块从当前和先前的视频帧。作为最后一步，两类支持向量机分类器被用来对候选区域进行分类。实验结果表明，该方法优于现有的国家的最先进的算法。
Every year, a large number of wildfires all over the world burn forested lands, causing adverse ecological, economic, and social impacts. Beyond taking precautionary measures, early warning and immediate response are the only ways to avoid great losses. To this end, in this paper we propose a computer vision approach for fire-flame detection to be used by an early-warning fire monitoring system. Initially, candidate fire regions in a frame are defined using background subtraction and color analysis based on a nonparametric model. Subsequently, the fire behavior is modeled by employing various spatio-temporal features, such as color probability, flickering, spatial, and spatio-temporal energy, while dynamic texture analysis is applied in each candidate region using linear dynamical systems and a bag-of-systems approach. To increase the robustness of the algorithm, the spatio-temporal consistency energy of each candidate fire region is estimated by exploiting prior knowledge about the possible existence of fire in neighboring blocks from the current and previous video frames. As a final step, a two-class support vector machine classifier is used to classify the candidate regions. Experimental results have shown that the proposed method outperforms existing state-of-the-art algorithms.