To improve the recognition rate and reduce the false-recognition rate in real-time detection of flame in video surveillance,a fast flame recognition algorithm based on multi-feature logarithm regression model was proposed.Firstly,the image was segmented according to the chromaticity of the flame,and the Candidate Fire Region (CFR) was obtained by subtracting the moving target image with reference image.Secondly the features of the CRF such as area change rate,circularity,number of sharp corners and centroid displacement were extracted to establish the logarithmic regression model.Then,a total of 300 images including flame and non-flame images,which were got from National Institute of Standards and Technology (NIST),Computer Vision laboratory of Inha University (ICV),Fire detection based on computer Vision (VisiFire) and the experimental library consisting of the candle and paper combustion were used to parametric learning.Finally,8 video clips including 11 071 images were used to validate the proposed algorithm.The experimental results show that the True Positive Rate (TPR) and True Negative Rate (TNR) of the proposed algorithm are 93％ and 98％ respectively.The average time of identification is 0.058 s/frame.Because of its fast identification and high recognition rate,the proposed algorithm can be applied in embedded real-time flame image recognition.%为了提高实时视频监控中火焰识别率和降低误识率,提出了一种基于多特征量对数回归模型的火焰快速识别算法.首先,根据火焰的色度特征进行图像分割,通过运动目标与参考图像差分运算获取火焰候选区域(CFR);然后提取候选区域的面积变化率、圆形度、尖角个数以及质心位移等特征量,建立火焰的对数回归快速识别模型;其次采用美国国家标准与技术研究院(NIST)、仁荷大学计算机视觉实验室(ICV)和基于计算机视觉的火灾探测(VisiFire)实验库以及自制蜡烛、纸燃烧火焰中的火焰和非火焰图像中的300幅进行参数学习;最后选取实验数据库中8段视频共11071幅图像进行识别算法检验.测试结果表明,所提算法的真正率(TPR)达到93％、真负率(TNR)达到98％,识别平均用时0.058s/帧.所提算法识别速度快且识别率高,可以应用于嵌入式实时图像火焰识别.
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