Abstract:

In order to improve the accuracy of flame recognition in video, a flame recognition algorithm based on Codebook was proposed. The algorithm which combined with static and dynamic features of flame was innovatively applied with YUV color space in Codebook background model to detect flame region, and update the background regularly. Firstly, the algorithm extracted frames from video, and used the liner relation between R, G, B component as the color model to get the flame color candidate area. Second, because of the advantage of the YUV color space, the images were transformed from RGB format to YUV format, a flame color dynamic prospect was extracted with background learning and background subtraction by using Codebook background model. At last, Back Propagation (BP) neural network was trained with the features vectors such as flame area change rate, flame area overlap rate and flame centroid displacement. Flame was judged by using the trained BP neural network in video. The recognition accuracy of the proposed algorithm in the complex video scene was above 96% in fixed camera position and direction videos. The experimental results show that compared with three state-of-art detection algorithms, the proposed algorithm has higher accuracy and lower misrecognition rate.

Key words: video, flame, YUV color space, Codebook background model, Back Propagation (BP) neural network

摘要：

为了提高视频火焰识别的准确度,提出了一种基于Codebook的火焰识别方法,创新地在YUV空间使用Codebook背景模型检测火焰区域,定期更新背景,综合火焰的动静态多特征进行火焰识别.首先,提取视频中的每一帧图像,利用原始图像中R、G、B三个分量间存在的线性关系作为颜色模型,初步提取火焰颜色区域; 然后,为了利用YUV颜色空间的有利特性,将颜色空间从RGB转化到YUV, 使用Codebook背景模型进行背景学习、背景差分,提取出具有火焰颜色的动态前景; 最后,利用火焰面积变化率、区域重叠率、质心位移这3个特征来训练反向传播(BP)神经网络,通过训练好的神经网络判断视频图像是否存在火焰.选取相机位置以及方向固定的视频进行实验,所提算法在复杂的视频场景中的识别准确度达到96%以上.实验结果表明,所提算法有效提高识别的准确度,同时降低多种干扰物场景的误判率.

关键词: 视频, 火焰, YUV 颜色空间, Codebook背景模型, 反向传播神经网络