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基于多特征融合技术的火焰视频探测方法
  • 期刊名称：燃烧科学与技术
  • 时间：2013
  • 页码：227-233
  • 分类：TP391.41[自动化与计算机技术—计算机应用技术;自动化与计算机技术—计算机科学与技术]
  • 作者机构：[1]中国科学技术大学火灾科学国家重点实验室,合肥230027, [2]公安部四川消防研究所,成都610036, [3]清华大学公共安全研究中心,北京100084, [4]东京大学工学院,东京113-8656, [5]浙江安全生产科学研究院,杭州310012
  • 相关基金：国家自然科学基金资助项目（51036007）
  • 相关项目：高原低压低氧特殊环境下火灾防治的热物理问题
作者： 荣建忠|姚卫|高伟|姚嘉杰|陈娟|汪箭|
关键词： 火焰视频图像探测, 统计地形特征, BP神经网络, 多特征融合, fire video detection, statistical landscape feature, BP neural network, multi-features fusion
中文摘要：

为了提高现有火焰视频探测技术的有效性，对火焰候选区域在时间和空间上的特征进行了提取，引入并改进了统计地形特征的纹理描述方法．基于神经网络算法，分析了共计57个火焰图像特征，如颜色、边界粗糙度和圆形度，通过将多特征进行融合以达到准确快速地识别火焰图像的效果．笔者利用多种场景下的视频录像以及现场实时监控场景进行了系统性能测试，结果表明，本研究提出的火灾视频探测系统具有较快的图像处理速度和较高的探测率．

英文摘要：

To improve the effectivity of existing fire image detection technology, this study extracts the spatial and time features from the candidate flame region, and further develops a pattern analysis method for the statistical land-scape feature of the flame region. A total of 57 flame features, such as flame color characteristics and boundary roughness and roundness, are analyzed by a BP neural network algorithm to fuse the multi-features and then to rec-ognize fire occurrence more accurately and quickly. In this paper, using a variety of scenarios and real-time video scene for the system performance test, results show that, the proposed fire video detection system can improve im-age processing speed and have higher accuracy.

同期刊论文项目
高原低压低氧特殊环境下火灾防治的热物理问题
期刊论文 89 会议论文 3
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