CAJ | 학술논문

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。
침대전통시빈형화염검측산법오보솔고、국한성강등문제，제출일충사보화염검측산법。수선이용일충자괄응혼합고사모형(GMM)검측시빈서렬중적운동목표；연후채용모호C균치(FCM)취류산법분할의사화염구역여비화구역；재제취의사화염구역적면적변화、표면불균도등시공특정삼수；최후장저사특정삼수수입훈련호적지지향량궤( SVM )분류기이식별화염구역。실험결과표명，산법불단재제고료검측솔적동시강저료오검솔，이차괄용범위엄，시일충유효적화염검측산법。
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.