结合FCM聚类与SVM的火焰检测算法

针对传统视频型火焰检测算法误报率高、局限性强等问题，提出一种四步火焰检测算法。首先利用一种自适应混合高斯模型（GMM）检测视频序列中的运动目标；然后采用模糊C 均值（FCM）聚类算法分割疑似火焰区域与非火区域；再提取疑似火焰区域的面积变化、表面不均度等时空特征参数；最后将这些特征参数输入训练好的支持向量机（SVM）分类器以识别火焰区域。实验结果表明，算法不但在提高了检测率的同时降低了误检率，而且适用范围广，是一种有效的火焰检测算法。

Fire detection algorithm using FCM clustering and SVM

An effective, four-stage fire-detection algorithm used to automatically detect fire in video images was presented in this paper. An adaptive Gaussian mixture model was used to detect moving regions in a video clip. A fuzzy C-means (FCM) algorithm was adopted to segment the candidate fire regions (fire and fire-colored objects) from these moving regions based on the color of fire. Some special parameters were extracted based on the tempo-spatial characteristics of fire regions; these parameters included the area randomness, surface roughness and motion estimation of fire. Finally, these parameters extracted from the third stage were used as input feature vectors to train a support vector machine(SVM) classifier, which was then used by the fire alarm to distinguish between fire and non-fire. Experimental results indicate that the proposed method outperforms other fire detection algorithms, providing high reliability and a low false alarm rate.