Computer Engineering and Applications ›› 2014, Vol. 50 ›› Issue (5): 165-168.
Previous Articles Next Articles
JIA Yang, WANG Huiqin, HU Yan, DANG Bo
Online:
Published:
贾 阳,王慧琴,胡 燕,党 勃
Abstract: In order to improve the fire detection rate based on video monitoring in spacious buildings, a fire detecting algorithm based on improved hierarchical cluster and Support Vector Machines(SVM) is proposed. Firstly suspected targets are detected with pixel motion accumulating method after color detection with a proper color model and the targets number is reduced with an improved hierarchical cluster method. Then the features, inter-frame correlation, area rate, centroid offset, average brightness, proportion of green and red are extracted. Finally the features are entered into the SVM to make a decision. The experimental results show that the cluster efficiency is improved, the limitation of threshold dependence is overcome, and it is suitable for image fire detection in spacious buildings.
Key words: flame detection, improved hierarchical cluster, Support Vector Machines(SVM), data sampling
摘要: 为了提高大空间建筑内实时监控的火灾检出率,提出基于改进分层聚类和支持向量机(SVM)的火灾识别算法。首先建立火焰颜色模型,用像素运动累积法获取疑似目标,借助改进层次聚类法对其进行合并,形成少量疑似区域。然后提取疑似区域相邻帧间相关性、面积变化率、质心偏移距离、红绿分量比、平均亮度这五个特征量。最后将特征输入到SVM进行二分类,判断是否有火。实验结果表明该算法提高了聚类算法在实际应用中的效率,克服了已有火灾识别算法过分依赖阈值的局限性,适用于室内大空间基于视频监控的火灾探测。
关键词: 火焰识别, 改进层次聚类, 支持向量机, 数据采样
JIA Yang, WANG Huiqin, HU Yan, DANG Bo. Flame detection algorithm based on improved hierarchical cluster and support vector machines[J]. Computer Engineering and Applications, 2014, 50(5): 165-168.
贾 阳,王慧琴,胡 燕,党 勃. 基于改进层次聚类和SVM的图像型火焰识别[J]. 计算机工程与应用, 2014, 50(5): 165-168.
0 / Recommend
Add to citation manager EndNote|Ris|BibTeX
URL: http://cea.ceaj.org/EN/
http://cea.ceaj.org/EN/Y2014/V50/I5/165