一种轻量化实时人体姿势检测模型研究与应用

doi:10.16182/j.issn1004731x.joss.20-FZ0308

系统仿真学报 ›› 2020, Vol. 32 ›› Issue (11): 2155-2165.doi: 10.16182/j.issn1004731x.joss.20-FZ0308

一种轻量化实时人体姿势检测模型研究与应用

朱洪堃¹, 殷佳炜¹, 冯文宇¹, 华亮¹, 费敏锐², 张堃^1*

1.南通大学电气工程学院,江苏南通 226000;
2.上海大学机电工程与自动化学院,上海市电站自动化技术重点实验室,上海 210053

收稿日期:2020-06-05 修回日期:2020-07-14 出版日期:2020-11-18 发布日期:2020-11-17
作者简介:朱洪堃(1996-),男,河南,硕士生,研究方向为计算机视觉。
基金资助:
江苏省六大人才高峰项目(XNY-039),江苏省高等学校自然科学研究重大项目(19KJA350002),国家级大学生创新创业训练计划(202010304065Z)

Research and Application of a Lightweight Real-time Human Posture Detection Model

Zhu Hongkun¹, Yin Jiawei¹, Feng Wenyu¹, Hua Liang¹, Fei Minrui², Zhang Kun^1*

1. School of Electrical Engineering,Nantong University,Nantong 226000,China;
2.School of Mechatronic Engineering and Automation,Shanghai University,Shanghai Key Laboratory of Power Station Automation Technology,Shanghai 210053,China

Received:2020-06-05 Revised:2020-07-14 Online:2020-11-18 Published:2020-11-17

摘要/Abstract

摘要： 传统的OpenPose模型在人体姿势检测方面精度较好但速度较慢。为了在保证检测精度的前提下加快检测速度、缩小模型,在传统OpenPose模型基础上,使用添加二阶项融合的残差网络提取底层特征,再通过L1范数权值对训练后的模型进行权值修剪,提出了改进型OpenPose模型。实验结果表明,在检测精度大致等同原模型情况下,模型大小缩小至约8%,参数减少近83%,检测速度提升约5倍。将改进的OpenPose模型应用到仰卧起坐体育健康测试中,结果表明该模型对仰卧起坐动作检测精度达到97%。

关键词: 人体姿势检测, OpenPose模型, 残差网络, 权值修剪

Abstract: The traditional OpenPose model has good accuracy but slow speed in human posture detection. In order to accelerate the detection speed and reduce the model on condition of the detection precision, based on the traditional OpenPose model, the residual network with second-order term fusion is used to extract the low-level features, the weights of the trained model are pruned by the L1 norm weight, and an improved OpenPose model is proposed. Experiments show that when the detection accuracy is approximately equal to original model, the model size reduces to about 8%, the parameters reduces by nearly 83%, and the detection speed increases by 5 times. The improved OpenPose model is applied to the physical fitness test of sit-ups, and the results show that the detection accuracy of the model can reach 97%.

Key words: human pose detection, OpenPose model, Residual network system, weight pruning

中图分类号:

TP273
TH89

朱洪堃, 殷佳炜, 冯文宇, 华亮, 费敏锐, 张堃. 一种轻量化实时人体姿势检测模型研究与应用[J]. 系统仿真学报, 2020, 32(11): 2155-2165.

Zhu Hongkun, Yin Jiawei, Feng Wenyu, Hua Liang, Fei Minrui, Zhang Kun. Research and Application of a Lightweight Real-time Human Posture Detection Model[J]. Journal of System Simulation, 2020, 32(11): 2155-2165.

参考文献

[1] Fang H,Xie S,Tai Y W,et al.RMPE:Regional Multi-person Pose Estimation [C].The 2017 IEEE International Conference on Computer Vision(ICCV),Venice.Italy:IEEE Press,2017:2353-2362.
[2] He K,Gkioxari G,Dollar P,et al.Mask R-CNN [C].The 2017 IEEE International Conference on Computer Vision(ICCV).Venice,Italy:IEEE Press,2017:2980-2988.
[3] Cao Z,Simon T,Wei S,et al.Realtime Multi-person 2D Pose Estimation Using Part Affinity Fields[C].Computer vision and pattern recognition(CVPR).Puerto Rico,USA:IEEE Press,2017:1302-1310.
[4] Wei S,Ramakrishna V,Kanade T,et al.Convolutional Pose Machines[C].Computer Vision and Pattern Recognition(CVPR).Las Vegas,USA:IEEE Press,2016:4724-4732.
[5] 周意乔,徐昱琳.基于双向LSTM的复杂环境下实时人体姿势识别[J].仪器仪表学报,2020,41(3):192-201.
Zhou Yiqiao,Xu Yulin.Real-time Human Posture Recognition in Complex Environment Based on Bidirectional LSTM[J].Chinese Journal of Scientific Instrument,2020,41(3):192-201.
[6] 周德良.OpenPose结合BP网络的行为检测方法[J].贵州大学学报(自然科学版),2020,37(3):87-92.
Zhou Deliang.Behavior Detection Method of OpenPose Combined With BP Network[J].Journal of Guizhou University(Natural Science Edition),2020,37(3):87-92.
[7] 郭园,郭晨旭,时新,等.基于OpenPose学习坐姿分析的桌椅人机适应性研究[J].林业工程学报,2020,5(2):179-185.
Guo Yuan,Guo Chenxu,Shi Xin,et al.Study on Human-machine Adaptability of Tables and Chairs Based on OpenPose Learning Sitting Posture Analysis[J].Journal of Forestry Engineering,2020,5(2):179-185.
[8] 彭秋萍,万莉莉,孙梦圆,等.基于机器视觉的扶梯自动急停系统[J].计算机系统应用,2020,29(3):87-92.
Peng Qiuping,Wan Lili,Sun Mengyuan,et al.Automatic Emergency Stop System of Escalator Based on Machine Vision[J].Application of Computer System,2020,29(3):87-92.
[9] Szegedy C,Liu W,Jia Y,et al.Going deeper with convolutions[C].The IEEE Conference on Computer Vision and Pattern Recognition(ICCV).Boston,USA:IEEE Press,2015:1-9.
[10] Costillareyes O,Verarodriguez R,Scully P J,et al.Analysis of Spatio-Temporal Representations for Robust Footstep Recognition with Deep Residual Neural Networks[J].IEEE Transactions on Pattern Analysis and Machine Intelligence(S0162-8828),2019,41(2):285-296.
[11] 刘望,孙金玉,马世伟.基于时空特征金字塔网络的动作时序检测方法[J].系统仿真学报,2019,31(11):2382-2387.
Liu Wang,Sun Jinyu,Ma Shiwei.A Temporal Action Detection Algorithm Based on Spatio-Temporal Feature Pyramid Network[J].Journal of System Simulation,2019,31(11):2382-2387.
[12] Liao Z,Wang J,Zhang S,et al.Minimizing Movement for Target Coverage and Network Connectivity in Mobile Sensor Networks[J].IEEE Transactions on Parallel and Distributed Systems(S1045-9219),2015,26(7):1971-1983.
[13] He K,Zhang X,Ren S,et al.Deep Residual Learning for Image Recognition[C].Computer Vision and Pattern Recognition(CVPR).Las Vegas,USA:IEEE Press,2016:770-778.
[14] Wang Y,Xie L,Liu C,et al.SORT:Second-Order Response Transform for Visual Recognition[C].International Conference on Computer Vision(ICCV).Venice,Italy:IEEE Press,2017:1368-1377.
[15] 石敏,姚瀚钦,李淳芃,等.基于深度Alignment网络的足部测量[J].系统仿真学报,2020,32(7):1267-1278.
Shi Min,Yao Hanqin,Li Chunpeng,et al.Foot Measurement Based on Deep Alignment Network[J].Journal of System Simulation,2020,32(7):1267-1278.
[16] Huang G,Liu S,Der Maaten L V,et al.CondenseNet:An Efficient DenseNet Using Learned Group Convolutions [C].Computer vision and pattern recognition(CVPR).Salt Lake City,USA:IEEE Press,2018:2752-2761.
[17] Zhou Z,Zhou W,Li H,et al.Online Filter Clustering and Pruning for Efficient Convnets[C].International conference on image processing(ICIP).Athens,Greece:IEEE Press,2018:11-15.
[18] 刘小雍,熊中刚,阎昌国.逼近误差的范数最小化的区间模糊模型建模[J].系统仿真学报,2018,30(3):1203-1209.
Liu Xiaoyong,Xiong Zhonggang,Yan Changguo.Interval Fuzzy Modeling Based on Minimizing-norm on Approximation Error[J].Journal of Forestry Engineering,2018,30(3):1203-1209.
[19] 徐嘉荟.基于模型剪枝的神经网络压缩技术研究[J].信息通信,2019(12):165-167.
Xu Jiahui.Research on Neural Network Compression Technology Based on Model Pruning[J].Information & Communications,2019(12):165-167.
[20] Duan P,Wang T,Cui M,et al.Multi-person Pose Estimation Based on a Deep Convolutional Neural Network[J].Journal of Visual Communication and Image Representation(S1047-3203),2019,61:245-252.

一种轻量化实时人体姿势检测模型研究与应用

Research and Application of a Lightweight Real-time Human Posture Detection Model

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