深度学习的人体图像半自动标注系统

doi:10.13190/j.jbupt.2020-181

北京邮电大学学报 ›› 2021, Vol. 44 ›› Issue (1): 104-109.doi: 10.13190/j.jbupt.2020-181

深度学习的人体图像半自动标注系统

高慧¹, 张继威¹, 来扬², 王文东³

1. 北京邮电大学计算机学院(国家示范性软件学院), 北京 100876;
2. 中国船舶工业系统工程研究院, 北京 100036;
3. 北京邮电大学网络与交换技术国家重点实验室, 北京 100876

收稿日期:2020-09-23 出版日期:2021-02-28 发布日期:2021-09-30
通讯作者: 王文东(1963-),男,教授,博士生导师,E-mail:wdwang@bupt.edu.cn. E-mail:wdwang@bupt.edu.cn
作者简介:高慧(1986-),男,讲师,硕士生导师.
基金资助:
中央高校基本科研业务费提升科技创新能力行动计划项目（2019XD-A12）；北京市自然科学基金-海淀原始创新联合基金项目（L182034）

Deep Learning Based Semi-Automatic Labeling System for Human Images

GAO Hui¹, ZHANG Ji-wei¹, LAI Yang², WANG Wen-dong³

1. School of Computer Science(National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing 100876, China;
2. Systems Engineering Research Institute of China State Shipbuilding Corporation, Beijing 100036, China;
3. State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China

Received:2020-09-23 Online:2021-02-28 Published:2021-09-30

摘要/Abstract

摘要： 针对目前数据标注过于依赖硬件、手动数据标注效率低下的问题，提出了基于深度学习的人体图像半自动标注系统.系统通过对算法进行改进，增加人体关键点个数进行特征提取和加入运动信息的约束，提高了视频分阶段标注的准确率.使用真实数据集仿真实验证明了通过深度学习算法进行数据标注的可行性，并且使用半自动标注的速度快、准确率高.

关键词: 图像标注, 半自动, 深度学习, 人体重建

Abstract: In view of the problem that data labeling is too dependent on hardware and manual data labeling is inefficient,a semi-automatic labeling system for human images based on deep learning is proposed. By improving the algorithm,the system increases the number of key points of the human body for feature extraction and adds motion information constraints,which improves the accuracy of video staged annotation. Experiments that employs real data sets prove the feasibility of data labeling by deep learning algorithm,and using deep learning algorithms for semi-automatic labeling is faster and more accurate.

Key words: image labeling, semi-automatic, deep learning, human reconstruction

中图分类号:

TP29

高慧, 张继威, 来扬, 王文东. 深度学习的人体图像半自动标注系统[J]. 北京邮电大学学报, 2021, 44(1): 104-109.

GAO Hui, ZHANG Ji-wei, LAI Yang, WANG Wen-dong. Deep Learning Based Semi-Automatic Labeling System for Human Images[J]. Journal of Beijing University of Posts and Telecommunications, 2021, 44(1): 104-109.

参考文献

[1] 袁柳, 李战怀, 陈世亮. 基于本体的Deep web数据标注[J]. 软件学报, 2008, 19(2):237-245. Yuan Liu, Li Zhanhuai, Chen Shiliang. Ontology-based annotation for deep web data[J]. Journal of Software, 2008, 19(2):237-245.
[2] Deng Jia, Dong Wei, Socher R, et al. Imagenet:a large-scale hierarchical image database[C]//2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition(CVPR 2009). Florida:IEEE, 2009:20-25.
[3] 周立君, 汪涛. 亚马逊土耳其机器人:科学研究的众包网络平台研究综述[J]. 科技进步与对策, 2014(8):162-166. Zhou Lijun, Wang Tao. Amazon mechanical turk:a survey on science research of crowdsourcing platform[J]. Science & Technology Progress and Policy, 2014(8):162-166.
[4] 肖玲, 潘浩. 基于WiFi信号的人体动作识别系统[J]. 北京邮电大学学报, 2018, 41(3):119-124. Xiao Ling, Pan Hao. Human activity recognition system based on WiFi signal[J]. Journal of Beijing University of Posts and Telecommunications, 2018, 41(3):119-124.
[5] 周全, 王磊, 周亮, 等. 基于多尺度上下文的图像标注算法[J]. 自动化学报, 2014(12):276-281. Zhou Quan, Wang Lie, Zhou Liang, et al. Multi-scale contextual image labeling[J]. ACTA Automatica Sinica, 2014(12):276-281.
[6] Srihari R K, Zhang Zhongfei. Show&tell:a semi-automated image annotation system[J]. IEEE Multimedia, 2000, 7(3):61-71.
[7] 税留成, 刘卫忠, 冯卓明. 基于生成式对抗网络的图像自动标注[J]. 计算机应用, 2019(7):2129-2133. Shui Liucheng, Liu Weizhong, Feng Zhuoming. Automatic image annotation based on generative adversarial network[J]. Journal of Computer Applications, 2019(7):2129-2133.
[8] Lassner C, Romero J, Kiefel M, et al. Unite the people:closing the loop between 3D and 2D human representations[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition(CVPR 2017). Hawaii:IEEE, 2017:6050-6059.
[9] Kanazawa A, Black M J, Jacobs D W, et al. End-to-end recovery of human shape and pose[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR). Salt Lake City:IEEE, 2018:7122-7131.
[10] Chen Yilun, Wang Zhicheng, Peng Yuxiang, et al. Cascaded pyramid network for multi-person pose estimation[C]//2018 IEEE Conference on Computer Vision and Pattern Recognition(CVPR 2018). Salt Lake City:IEEE, 2018:7103-7112.
[11] He Kaiming, Gkioxari G, Dollar P, et al. Mask R-CNN[C]//2017 IEEE International Conference on Computer Vision(ICCV 2017). Vinice:IEEE, 2017:2961-2969.
[12] Simonyan K, Zisserman A. Two-stream convolutional networks for action recognition in videos[J]. Advances in Neural Information Processing Systems, 2014(1):568-576.
[13] Chéron G, Laptev I, Schmid C. P-CNN:pose-based CNN features for action recognition[C]//2015 IEEE International Conference on Computer Vision(ICCV 2015). Santiago:IEEE, 2015:3218-3226.
[14] Brox T,Bruhn A,Papenberg N,et al.High accuracy optical flow estimation based on a theory for warping[C]//European Conference on Computer Vision.Heidelberg:Springer, 2004:25-36.
[15] Qiu Zhaofan, Yao Ting, Mei Tao. Learning spatio-temporal representation with pseudo-3D residual networks[C]//2017 IEEE International Conference on Computer Vision(ICCV). Vinice:IEEE, 2017:5534-5542.

深度学习的人体图像半自动标注系统

Deep Learning Based Semi-Automatic Labeling System for Human Images

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	刘阳, 滕颖蕾, 牛涛, 郅佳琳. 基于深度强化学习的滤波器剪枝方案[J]. 北京邮电大学学报, 2023, 46(3): 31-36.
[2]	靳梦凡, 黄智濒, 储志强. 复杂环境下多模态指导的点云补全方法[J]. 北京邮电大学学报, 2023, 46(3): 103-108.
[3]	荣震宇刘建毅. 基于Transformer和MLP的眼底血管分割算法[J]. 北京邮电大学学报, 2023, 46(1): 26-31.
[4]	刘金桐, 杨国兴, 刘晓鸿, 王光宇. 针对心电图分类模型的平滑攻击算法[J]. 北京邮电大学学报, 2022, 45(4): 44-50.
[5]	黄柳婷, 刘可欣, 牛凯, 常春, 贺志强. 基于深度学习的哮喘患者CT影像黏液栓自动识别[J]. 北京邮电大学学报, 2022, 45(4): 58-63.
[6]	柳长源, 虎浩媛, 毕晓君. 双线性融合网络的驾驶员分心行为识别[J]. 北京邮电大学学报, 2022, 45(2): 79-84.
[7]	赵海英, 朱会, 侯小刚. 基于改进EMA单元的传统服饰图像语义分割[J]. 北京邮电大学学报, 2022, 45(1): 69-74.
[8]	张滨宇, 赵衍运, 杜昀昊, 万俊峰, 佟知航. 一种基于深度学习的PCB图像字符检测方法[J]. 北京邮电大学学报, 2022, 45(1): 108-114.
[9]	贾珺, 冯春燕, 夏海轮, 张天魁, 李成钢. 基于样本均衡与特征交互的通信网络故障预测方法[J]. 北京邮电大学学报, 2021, 44(6): 59-66.
[10]	普运伟, 郭江, 刘涛涛, 吴海潇. 基于多学习单元卷积神经网络的雷达辐射源信号识别[J]. 北京邮电大学学报, 2021, 44(6): 74-82.
[11]	王艺霏, 莫爽, 吴文睿, 范少华, 肖丁. 基于内外卷积网络的网络入侵检测[J]. 北京邮电大学学报, 2021, 44(5): 94-100.
[12]	蒲悦逸, 王文涵, 朱强, 陈朋朋. 基于CNN-ResNet-LSTM模型的城市短时交通流量预测算法[J]. 北京邮电大学学报, 2020, 43(5): 9-14.
[13]	马向亮, 李冰, 杨丹, 黄克振, 段晓毅. 基于深度学习的类SM4算法S盒逆向分析[J]. 北京邮电大学学报, 2020, 43(5): 118-124.
[14]	张天麒, 康波, 孟祥飞, 刘奕琳, 周颖. 基于U-Net的颅内出血识别算法[J]. 北京邮电大学学报, 2020, 43(3): 92-98.
[15]	马素刚, 赵祥模, 侯志强, 王忠民, 孙韩林. 一种基于ResNet网络特征的视觉目标跟踪算法[J]. 北京邮电大学学报, 2020, 43(2): 129-134.