Abstract: In order to improve fire detection adaptive ability, this paper proposes a video fire detection algorithm based on wavelet transform and support vector machine. Candidatefire regions are detected by the color of the flame probability model, and the features are the area changing, the high frequency of fire flicker frequency and energy change tendency, combined image processing technology with wavelet. It selects these features as a Support Vector Machine（SVM） input characteristic vector to realize fire recognition. Experimental results show that the proposed method has higher identification accuracy, strong ability to adapt to the environment.

Key words: fire detection, color model, wavelet transform, Support Vector Machine（SVM）

摘要： 为了提高火灾检测方法的环境适应能力，研究了一种基于小波变换和支持向量机的视频火灾识别算法。提出了火焰颜色概率模型对疑似火焰区域进行分割，经过小波变换分析疑似火焰区域高频子图能量信息，对其一维能量信息进行二次小波变换得能量变化趋势和闪烁频率。将提取火焰的能量变化趋势，闪烁频率和火焰面积变化率作为支持向量机的输入特征参数，实现了火灾识别。实验结果表明，该算法有较高的识别准确率，较强的环境适应能力。

关键词: 火焰探测, 颜色模型, 小波变换, 支持向量机