大空间建筑火源的精确定位方法  

PRECISE LOCALISATION METHOD FOR FIRE SOURCES IN LARGE-SPACE BUILDINGS

作　　者：毛颖[1] 王慧琴[1,2] 卢英[1,2] 秦立科[1] 

机构地区：[1]西安建筑科技大学信息与控制工程学院,陕西西安710055 [2]西安建筑科技大学管理学院,陕西西安710055

出　　处：《计算机应用与软件》2016年第2期169-172,共4页Computer Applications and Software

基　　金：教育部高等学校博士学科点专项科研基金项目(20126120110008);陕西省教育厅产业化项目(2011JG12);陕西省自然科学基础研究计划项目(2012JQ8021);教育厅专项科研项目(2013JK1144);西安建筑科技大学校青年基金项目(QN1429)

摘　　要：针对大空间建筑火源定位精度低和实时性差的问题,提出一种基于火焰图像熵值加速鲁棒性特征SURF(Speeded Up Robust Features)的改进遗传算法求解框架应用在大空间建筑火源定位中。在良好的火源探测下,首先用SURF算法提取火焰特征点,引入特征点熵值来减少误匹配,得到较好的匹配点对,进一步求解摄像机参数。通过改进选择、交叉、变异算子设计大空间建筑火源定位中摄像机标定的遗传算法,最终获得火源三维信息。实验结果表明,提出的新算法应用在大空间建筑火源定位中能更好恢复火源的三维深度信息。In order to solve the problems of low accuracy in fire source localisation and poor real-time property in large-space buildings, we proposed a solution framework of improved genetic algorithm, which is based on flame image entropy accelerating the robust feature （ SURF）, to apply in the fire source localisation of large-space buildings. Under the condition of good fire source detection, it first extracts flame feature points with SURF algorithm, then introduces the entropy of the feature points to reduce mismatching, and gets better matching-point pairs to further solve the parameters of video camera. We designed a genetic algorithm for camera calibration in fire source loc^lisation of large-space buildings by＇ improving the selection, crossover and mutation operators, and finally obtained the three-dimensional information of fire source. Experimental result illustrated that applying the proposed new algorithm in fire source localisation of large-space buildings can better restore 3D depth information of fire source.

关 键 词：摄像机标定 加速鲁棒性特征 火焰熵值 改进遗传算法 火源定位 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]