CAJ | 학술논문

针对单一的隐马尔科夫模型在图像型火灾探测中误报率偏高的问题，提出了隐马尔科夫模型和支持向量机相结合的图像型火焰识别算法。对捕获到的图像进行运动区域检测和颜色分析，提取疑似火焰区域，利用隐马尔科夫模型计算疑似区域与火焰模型的相似度，并输入到训练好的支持向量机进行二次识别。实验结果表明，与传统单一隐马尔科夫模型相比，该方法可以有效地降低误报率，提高火焰识别准确性。
침대단일적은마이과부모형재도상형화재탐측중오보솔편고적문제，제출료은마이과부모형화지지향량궤상결합적도상형화염식별산법。대포획도적도상진행운동구역검측화안색분석，제취의사화염구역，이용은마이과부모형계산의사구역여화염모형적상사도，병수입도훈련호적지지향량궤진행이차식별。실험결과표명，여전통단일은마이과부모형상비，해방법가이유효지강저오보솔，제고화염식별준학성。
In view of the high false alarm rate of image fire detection based on only Hidden Markov Model, a flame rec-ognition algorithm based on Hidden Markov Model and Support Vector Machines is proposed. Flame candidate regions of captured image are detected with the motion detection and color analysis. Then after matching the candidate regions and flame models, HMM is used to calculate the similarity values between them. The matching values are input into SVM for second recognition. The experimental results show that compared with HMM, the HMM-SVM hybrid model can effectively reduce the rate of false positives, and improve the flame recognition accuracy.