结合FCM聚类与SVM的火焰检测算法 |
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引用本文: | 李庆辉,李艾华,苏延召,马治明.结合FCM聚类与SVM的火焰检测算法[J].红外与激光工程,2014,43(5):1660-1666. |
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作者姓名: | 李庆辉 李艾华 苏延召 马治明 |
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作者单位: | 1.第二炮兵工程大学502 教研室,陕西 西安 710025; |
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基金项目: | 国家自然科学基金(61132008) |
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摘 要: | 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。
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关 键 词: | 火焰检测 混合高斯模型 模糊C 均值聚类 支持向量机 |
收稿时间: | 2013-09-22 |
Fire detection algorithm using FCM clustering and SVM |
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Affiliation: | 1.502 Faculty,Second Artillery Engineering University,Xi'an 710025,China;2.No 96111 Troops of Chinese People's Liberation Army,Hancheng 715400,China |
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Abstract: | 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. |
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