基于视频的行人视力状况分析展示系统
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摘要
视力是群体健康的重要指标之一,是建设健康城市的重要调查内容.传统调查群体视力的方法存在局限.本文采用深度学习的方式分析监控视频中行人的人脸属性,识别公共群体中视力障碍的数量和比例,并且分性别研究,作为区域人群群体健康的样本指标.针对视频中人脸属性的识别问题,引入人脸检测卷积神经网络来检测行人人脸,在此基础上提出了改进的人脸分析卷积神经网络,分别完成性别的识别及是否佩戴眼镜的识别.最后研究建立了以百度地图为基础的区域视力数据展示系统,并在Web端分街道和区域对男女视力障碍比例进行数据可视化展示,为接下来的实际应用打下基础.实验结果及系统展示表明,本文提出的方法能有效识别群体视力障碍情况,为群体视力健康调查工作提供了新思路.
Visual acuity is one of the most important indicators of group health and a vital survey content of building a healthy city. Traditional methods of investigating group vision have limitations. In this study, the pedestrian's face attributes in the surveillance video are analyzed by deep learning. We identify the number and proportion of visual impairments in public group and study them by gender, and use it as a sample index of regional population health. Aiming at the problem of face attributes recognition in video, the convolutional neural network for face detection is introduced to detect pedestrian's face. On this basis, an improved convolutional neural network for face analysis is proposed to recognize gender and whether or not to wear glasses. Finally, a regional visual data display system based on Baidu Map is established, and the visual data of the proportion of male and female visual impairment is displayed in streets and regions of the Web, which lays the foundation for the next practical application. The experimental results and system demonstration show that the proposed method can effectively identify group visual impairment and provide a new idea for group visual health survey.
Visual acuity is one of the most important indicators of group health and a vital survey content of building a healthy city. Traditional methods of investigating group vision have limitations. In this study, the pedestrian's face attributes in the surveillance video are analyzed by deep learning. We identify the number and proportion of visual impairments in public group and study them by gender, and use it as a sample index of regional population health. Aiming at the problem of face attributes recognition in video, the convolutional neural network for face detection is introduced to detect pedestrian's face. On this basis, an improved convolutional neural network for face analysis is proposed to recognize gender and whether or not to wear glasses. Finally, a regional visual data display system based on Baidu Map is established, and the visual data of the proportion of male and female visual impairment is displayed in streets and regions of the Web, which lays the foundation for the next practical application. The experimental results and system demonstration show that the proposed method can effectively identify group visual impairment and provide a new idea for group visual health survey.
引文
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20石学超,周亚同,池越.基于多层特征融合可调监督函数卷积神经网络的人脸性别识别.计算机应用研究,2019,36(4):940-944.
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23Eidinger E,Enbar R,Hassner T.Age and gender estimation of unfiltered faces.IEEE Transactions on Information Forensics and Security,2014,9(12):2170-2179.[doi:10.1109/TIFS.2014.2359646]
24Huang GB,Ramesh M,Berg T,et al.Labeled faces in the wild:A database for studying face recognition in unconstrained environments.Technical Report 07-49. Amherst:University of Massachusetts,2007.
25杜传明.百度地图API在小型地理信息系统中的应用.测绘与空间地理信息,2011,34(2):152-153,156.[doi:10.3969/j.issn.1672-5867.2011.02.052]
26冀潇,李杨.采用ECharts可视化技术实现的数据体系监控系统.计算机系统应用,2017,26(6):72-76.[doi:10.15888/j.cnki.csa.005782]
22010年全国学生体质与健康调研结果.中国学校卫生,2011,32(9):1024,1026.
3董彦会,刘慧彬,王政和,等.2005-2014年中国7~18岁儿童青少年近视流行状况与变化趋势.中华预防医学杂志,2017,51(4):285-289.
4Holden BA,Fricke TR,Wilson DA,et al.Global prevalence of myopia and high myopia and temporal trends from 2000 through 2050.Ophthalmology,2016,123(5):1036-1042.[doi:10.1016/j.ophtha.2016.01.006]
5杜连心,吕天斌,覃建,等.郑州市金水区小学生视力状况调查.眼科新进展,2016,36(6):550-554.
6周珂晶,黄玉庚,姜黎,等.学龄前儿童近视环境因素调查分析.现代实用医学,2018,30(1):108-109.[doi:10.3969/j.issn.1671-0800.2018.01.055]
7朱梦男,何理烨,梁亮.湖北省某高校医学院大学生视力调查及影响因素分析.河北医药,2017,39(3):460-462.
8他卉,曹应琼,何琳,等.四川省中小学校教学环境与视力分析.现代预防医学,2018,45(20):3748-3751,3759.
9Zhang SS,Benenson R,Schiele B,et al.CityPersons:Adiverse dataset for pedestrian detection.Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition.Honolulu,HI,USA,2017:4457-4465.
10Fang K,Xiang Y,Li XC,et al.Recurrent autoregressive networks for online multi-object tracking.Proceedings of 2018 IEEE Winter Conference on Applications of Computer Vision.Lake Tahoe,NV,USA,2018:466-475.
11Arora S,Bhatia MPS.A robust approach for gender recognition using deep learning.Proceedings of the 9th International Conference on Computing,Communication and Networking Technologies.Bangalore,India,2018:1-6.
12杨德培.视频监控中人物属性识别方法的研究[硕士学位论文].成都:电子科技大学,2016.
13韩龙玫,卿粼波.视频大数据在城市公共空间规划领域的应用前景探索.四川建筑,2017,37(3):12-15.
14Ren SQ,He KM,Girshick R,et al.Faster R-CNN:Towards real-time object detection with region proposal networks.IEEE Transactions on Pattern Analysis and Machine Intelligence,2017,39(6):1137-1149.[doi:10.1109/TPAMI.2016.2577031]
15Liu W,Anguelov D,Erhan D,et al.SSD:Single shot MultiBox detector.Proceedings of the 14th European Conference on Computer Vision.Amsterdam,the Netherlands.2016.21-37.
16Zhang KP,Zhang ZP,Li ZF,et al.Joint face detection and alignment using multitask cascaded convolutional networks.IEEE Signal Processing Letters,2016,23(10):1499-1503.[doi:10.1109/LSP.2016.2603342]
17Simonyan K,Zisserman A.Very deep convolutional networks for large-scale image recognition.Proceedings of 2015 International Conference on Learning Representations.San Diego,CA,USA.2015.arXiv:1409.1556.
18He KM,Zhang XY,Ren SQ,et al.Deep residual learning for image recognition.Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition.Las Vegas,NV,USA,2016:770-778.
19张婷,李玉鑑,胡海鹤,等.基于跨连卷积神经网络的性别分类模型.自动化学报,2016,42(6):858-865.
20石学超,周亚同,池越.基于多层特征融合可调监督函数卷积神经网络的人脸性别识别.计算机应用研究,2019,36(4):940-944.
21Shen W,Wang XG,Wang Y,et al.DeepContour:A deep convolutional feature learned by positive-sharing loss for contour detection.Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition.Boston,MA,USA.2015.3982-3991.
22Jain V,Learned-Miller E.FDDB:A benchmark for face detection in unconstrained settings.Technical Report UM-CS-2010-009.Amherst:University of Massachusetts,2010.
23Eidinger E,Enbar R,Hassner T.Age and gender estimation of unfiltered faces.IEEE Transactions on Information Forensics and Security,2014,9(12):2170-2179.[doi:10.1109/TIFS.2014.2359646]
24Huang GB,Ramesh M,Berg T,et al.Labeled faces in the wild:A database for studying face recognition in unconstrained environments.Technical Report 07-49. Amherst:University of Massachusetts,2007.
25杜传明.百度地图API在小型地理信息系统中的应用.测绘与空间地理信息,2011,34(2):152-153,156.[doi:10.3969/j.issn.1672-5867.2011.02.052]
26冀潇,李杨.采用ECharts可视化技术实现的数据体系监控系统.计算机系统应用,2017,26(6):72-76.[doi:10.15888/j.cnki.csa.005782]
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