| 基于LBP统计特征的低分辨率安全帽识别 |
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| 引用本文： | 周艳青,薛河儒,姜新华,孙海鑫,寻言言.基于LBP统计特征的低分辨率安全帽识别[J].计算机系统应用,2015,24(7):211-215. |
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| 作者姓名： | 周艳青 薛河儒 姜新华 孙海鑫 寻言言 |
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| 作者单位： | 内蒙古农业大学计算机与信息工程学院,呼和浩特,010018 |
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| 基金项目： | 公益性行业(农业)科研专项项目(201203041);教育部“春晖计划”项目(Z2009-1-01062);内蒙古农业大学科技创新团队项目(ZN201010) |
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| 摘 要： | 针对工地进出口的视频监控录像，考虑远距离低分辨率安全帽识别问题，探讨了低分辨率安全帽识别方法，分析了提取不同的特征和应用不同的分类器与识别率的关系.首先截取视频中的人头，获得大小为22*22的图像，然后分别提取图像的统计特征、局部二进制模式特征、快速主成分分析特征，再利用分类器和反向人工神经网络进行分类预测，最后计算测试样本的识别率.实验结果表明，提取图像的二进制模式统计特征，再结合反向人工神经网络的识别率效果最佳，识别率可达87.27%.
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| 关 键 词： | 低分辨率 局部二进制模式 统计特征 反向人工神经网络 识别率 |
| 收稿时间： | 2014/2/27 0:00:00 |
| 修稿时间： | 3/2/2015 12:00:00 AM |
Low-resolution Safety Helmet Image Recognition Combining Local Binary Pattern with Statistical Features |
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| ZHOU Yan-Qing,XUE He-Ru,JIANG Xin-Hu,SUN Hai-Xin and XUN Yan-Yan.Low-resolution Safety Helmet Image Recognition Combining Local Binary Pattern with Statistical Features[J].Computer Systems& Applications,2015,24(7):211-215. |
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| Authors: | ZHOU Yan-Qing XUE He-Ru JIANG Xin-Hu SUN Hai-Xin and XUN Yan-Yan |
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| Affiliation: | College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China;College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China;College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China;College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China;College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China |
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| Abstract: | According to the entrance and export of construction surveillance video, this paper discusses the low-resolution image recognition method of the safety helmet and deduces the relation of different features and classifiers with recognition rate in view of taking account of the low-resolution safety helmet recognition problem at the long distance. It first captured the head of video to obtain images of the size of 22×22, and then extracted the statistical features of each image, LBP and PCA features. Finally, the recognition rate of test sample was calculated by taking advantage of minimum distance classifier and BP artificial neural network. The experimental results show that the LBP statistical features in combination with BP artificial neural network can recognize the safety helmet effectively. The recognition rate reached 87.27%. |
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| Keywords: | low-resolution LBP statistic feature BP artificial neural network recognition rate |
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