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机构地区:[1]中国矿业大学(北京)机电与信息工程学院,北京100083
出 处:《煤炭科学技术》2015年第11期91-94,共4页Coal Science and Technology
基 金:国家自然科学基金重点资助项目(51134024);国家自然科学基金资助项目(51074169);国家高技术研究发展计划(863计划)资助项目(2012AA062203)
摘 要:为解决煤矿井下照度低、照度不均匀并且变化剧烈,缺乏颜色信息,井下人员视觉表观与背景相似,而给基于视觉的井下人员跟踪定位技术发展带来的难题,基于压缩感知理论,利用随机投影技术提出了一种简单、新颖、但有效的跟踪定位矿井视频图像中人员的方法,用非常稀疏的随机投影矩阵从图像多尺度纹理特征空间抽取目标特征构成目标模型,利用朴素贝叶斯分类器采用鉴别式方法确定跟踪目标位置,并用随机特征对目标模型进行自适应在线更新。在神东集团大柳塔煤矿采集的井下视频上试验结果表明:该算法对目标的遮挡、旋转及不均匀的环境照度和照度的剧烈变化都具有极强的鲁棒性,平均跟踪帧速率达50帧/s,满足实时性要求,可为基于视觉的井下人员定位技术提供参考。In order to solve a low illuminance,inhomogeneous illuminance,serious varied illuminance and lack of color information in the underground mine,due to the similar visual apparent and background of the personnel in underground mine would bring a difficulty to the technology development of the personnel positioning in the underground mine based on the visual sense,the application of the random projection technology proposed a simple,novel and effective method to trace and position the personnel in the mine video images based on the compressed sensing theory. A very sparse random projection matrix was applied to extract the target features from the multi dimension texture feature space of the images to form the target model,the Naive Bayes classification with the identification method was applied to determine the location of the tracing target and the random features were applied to the suitable update of the target model. The operation results of the underground video collected from Daliuta Mine of Shendong Group showed that the calculation method would have high robustness to the target shielding,rotary and uneven environment illuminance and illuminance serious variation. The average tracing frame speed could be 50 frame per s and could provide the references to the positioning technology of the underground personnel based on the visual sense.
关 键 词:矿井 人员跟踪 随机投影 压缩感知 多尺度 局部纹理特征
分 类 号:TD713[矿业工程—矿井通风与安全]
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