Research on multi-feature human pose model recognition based on one-shot learning

Li Guoyou; Li Chenguang; Wang Weijiang; Yang Mengqi; Hang Bingpeng

doi:10.12086/oee.2021.200099

Issue 2

Feb 2021

Turn off MathJax

Article Contents

JOURNAL OF MECHANICAL ENGINEERING > 2021 > 48(2): 200099.

Li Guoyou, Li Chenguang, Wang Weijiang, Yang Mengqi, Hang Bingpeng. Research on multi-feature human pose model recognition based on one-shot learning[J]. JOURNAL OF MECHANICAL ENGINEERING, 2021, 48(2): 200099. doi: 10.12086/oee.2021.200099

Citation:

Li Guoyou, Li Chenguang, Wang Weijiang, Yang Mengqi, Hang Bingpeng. Research on multi-feature human pose model recognition based on one-shot learning[J]. JOURNAL OF MECHANICAL ENGINEERING, 2021, 48(2): 200099. doi: 10.12086/oee.2021.200099

Citation:

PDF( 0 KB)

Research on multi-feature human pose model recognition based on one-shot learning

doi: 10.12086/oee.2021.200099

Funds:

Youth Fund for Science and Technology Research in Colleges and Universities of Hebei Province 2011139

Natural Science Foundation of Hebei Province F2012203111

More Information

Corresponding author: Li Chenguang, E-mail: 1498578260@qq.com
Received Date: 23 Mar 2020
Rev Recd Date: 02 Jun 2020

Abstract

Abstract

With the development of human-computer interaction, virtual reality, and other related fields, human posture recognition has become a hot research topic. Since the human body belongs to a non-rigid model and has time-varying characteristics, the accuracy and robustness of recognition are not ideal. Based on the KinectV2 somatosensory camera to collect skeletal information, this paper proposes a one-shot learning model matching method based on human body angle and distance characteristics. First, feature extraction is performed on the collected bone information, and the joint point vector angle and joint point displacement are calculated and a threshold is set. Secondly, the pose to be measured is matched with the template pose, and the recognition is successful if the threshold limit is met. Experimental results show that the method can detect and recognize human poses within the defined threshold in real-time, which improves the accuracy and robustness of recognition.
- pose model,
- skeleton data,
- one-shot learning,
- model matching,
- KinectV2

FullText(HTML)

References(27)

References

[1]	Henry P, Krainin M, Herbst E, et al. RGB-D mapping: using Kinect-style depth cameras for dense 3D modeling of indoor environments[J]. Int J Rob Res, 2012, 31(5): 647-663. doi: 10.1177/0278364911434148
[2]	黄国范, 李亚. 人体动作姿态识别综述[J]. 电脑知识与技术, 2013, 12(1): 133-135. https://www.cnki.com.cn/Article/CJFDTOTAL-DNZS201301045.htm Huang G F, Li Y. A survey of human action and pose recognition[J]. Comput Knowl Technol, 2013, 12(1): 133-135. https://www.cnki.com.cn/Article/CJFDTOTAL-DNZS201301045.htm
[3]	Shotton J, Sharp T, Kipman A, et al. Real-time human pose recognition in parts from single depth images[J]. Commun ACM, 2013, 56(1): 116-124. doi: 10.1145/2398356.2398381
[4]	严利民, 杜斌, 郭强, 等. 基于局部扫描法对倾斜指势的识别[J]. 光电工程, 2016, 43(12): 147-153. doi: 10.3969/j.issn.1003-501X.2016.12.023 Yan L M, Du B, Guo Q, et al. Recognize the tilt fingertips by partial scan algorithm[J]. Opto-Electron Eng, 2016, 43(12): 147-153. doi: 10.3969/j.issn.1003-501X.2016.12.023
[5]	李红波, 丁林建, 吴渝, 等. 基于Kinect骨骼数据的静态三维手势识别[J]. 计算机应用与软件, 2015, 14(9): 161-165. doi: 10.3969/j.issn.1000-386x.2015.09.039 Li H P, Ding L J, Wu Y, et al. Static three-dimensional gesture recognition based on Kinect skeleton data[J]. Comput Appl Softw, 2015, 14(9): 161-165. doi: 10.3969/j.issn.1000-386x.2015.09.039
[6]	Cao Z, Simon T, Wei S E, et al. Realtime multi-person 2D pose estimation using part affinity fields[C]//Conference on Computer Vision and Pattern Recognition(CVPR), 2017: 7291-7299.
[7]	Pfitscher M, Welfer D, Evaristo José Do Nascimento, et al. Article Users Activity Gesture Recognition on Kinect Sensor Using Convolutional Neural Networks and FastDTW for Controlling Movements of a Mobile Robot[J]. Inteligencia Artificial Revista Iberoamericana de Inteligencia Artificial, 2019, 22(63): 121-134. http://www.researchgate.net/publication/332227318_Article_Users_Activity_Gesture_Recognition_on_Kinect_Sensor_Using_Convolutional_Neural_Networks_and_FastDTW_for_Controlling_Movements_of_a_Mobile_Robot/download
[8]	赵海勇. 基于视频流的运动人体行为识别研究[D]. 西安: 西安电子科技大学, 2011. Zhao H Y. Research of human action recognition based on video stream[D]. Xi'an: Xidian University, 2011.
[9]	Xu W Y, Wu M Q, Zhao M, et al. Human action recognition using multilevel depth motion maps[J]. IEEE Access, 2019, 7: 41811-41822. doi: 10.1109/ACCESS.2019.2907720
[10]	Vinyals O, Blundell C, Lillicrap T, et al. Matching networks for one shot learning[C]//Proceedings of the 30th International Conference on Neural Information Processing Systems, 2016: 3637-3645.
[11]	朱大勇, 郭星, 吴建国. 基于Kinect三维骨骼节点的动作识别方法[J]. 计算机工程与应用, 2018, 54(20): 152-158. doi: 10.3778/j.issn.1002-8331.1706-0285 Zhu D Y, Guo X, Wu J G. Action recognition method using Kinect 3D skeleton data[J]. Comput Eng Appl, 2018, 54(20): 152-158. doi: 10.3778/j.issn.1002-8331.1706-0285
[12]	蔡兴泉, 涂宇欣, 余雨婕, 等. 基于少量关键序列帧的人体姿态识别方法[J]. 图学学报, 2019, 40(3): 532-538. https://www.cnki.com.cn/Article/CJFDTOTAL-GCTX201903017.htm Cai X Q, Tu Y X, Yu Y J, et al. Human posture recognition method based on few key frames sequence[J]. J Graph, 2019, 40(3): 532-538. https://www.cnki.com.cn/Article/CJFDTOTAL-GCTX201903017.htm
[13]	钱银中, 沈一帆. 姿态特征与深度特征在图像动作识别中的混合应用[J]. 自动化学报, 2019, 45(3): 626-636. https://www.cnki.com.cn/Article/CJFDTOTAL-MOTO201903016.htm Qian Y Z, Shen Y F. Hybrid of pose feature and depth feature for action recognition in static image[J]. Acta Autom Sin, 2019, 45(3): 626-636. https://www.cnki.com.cn/Article/CJFDTOTAL-MOTO201903016.htm
[14]	Monir S, Rubya S, Ferdous H S. Rotation and scale invariant posture recognition using Microsoft Kinect skeletal tracking feature[C]//2012 12th International Conference on Intelligent Systems Design and Applications (ISDA), 2012: 829-842.
[15]	郭同欢, 陈姚节, 林玲. 基于姿态角的双Kinect数据融合技术及应用[J]. 科学技术与工程, 2019, 19(29): 172-178. doi: 10.3969/j.issn.1671-1815.2019.29.028 Guo T H, Chen Y J, Lin L. Gesture recognition based on the gesture angle of dual Kinect[J]. Sci Technol Eng, 2019, 19(29): 172-178. doi: 10.3969/j.issn.1671-1815.2019.29.028
[16]	Wang P, Liu L Q, Shen C H, et al. Multi-attention network for one shot learning[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017: 2721-2729.
[17]	Yang Y, Saleemi I, Shah M. Discovering motion primitives for unsupervised grouping and one-shot learning of human actions, gestures, and expressions[J]. IEEE Trans Pattern Anal Mach Intell, 2013, 35(7): 1635-1648. doi: 10.1109/TPAMI.2012.253
[18]	Sundermeyer M, Alkhouli T, Wuebker J, et al. Translation modeling with bidirectional recurrent neural networks[C]//Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2014: 14-25.
[19]	Cheng X L, He M Y, Duan W J. Machine vision based physical fitness measurement with human posture recognition and skeletal data smoothing[C]//2017 International Conference on Orange Technologies (ICOT), 2017: 323-342.
[20]	Li Q M, Lin W X, Li J. Human activity recognition using dynamic representation and matching of skeleton feature sequences from RGB-D images[J]. Signal Process Image Commun, 2018, 68: 265-272. doi: 10.1016/j.image.2018.06.013
[21]	Zhang Z Q, Liu Y N, Li A, et al. A novel method for user-defined human posture recognition using Kinect[C]//2014 7th International Congress on Image and Signal Processing, 2014: 724-739.
[22]	Sagayam K M, Hemanth D J. Hand posture and gesture recognition techniques for virtual reality applications: a survey[J]. Virtual Real, 2017, 21(2): 91-107. doi: 10.1007/s10055-016-0301-0
[23]	Stephenson R M, Chai R, Eager D. Isometric finger pose recognition with sparse channel SpatioTemporal EMG imaging[J]. Annu Int Conf IEEE Eng Med Biol Soc, 2018, 2018: 5232-5235. http://www.ncbi.nlm.nih.gov/pubmed/30441518
[24]	Vishwakarma D K, Singh T. A visual cognizance based multi-resolution descriptor for human action recognition using key pose[J]. AEU-Int J Electron Commun, 2019, 107: 157-169. doi: 10.1016/j.aeue.2019.05.023
[25]	Liu X X, Feng X Y, Pan S J, et al. Skeleton tracking based on Kinect camera and the application in virtual reality system[C]//Proceedings of the 4th International Conference on Virtual Reality, 2018: 21-25.
[26]	Bulbul M F, Islam S, Ail H. 3D human action analysis and recognition through GLAC descriptor on 2D motion and static posture images[J]. Multimed Tools Appl, 2019, 78(15): 21085-21111. doi: 10.1007/s11042-019-7365-2
[27]	Agahian S, Negin F, Köse C. An efficient human action recognition framework with pose-based spatiotemporal features[J]. Eng Sci Technol, 2020, 23(1): 196-203. http://www.sciencedirect.com/science/article/pii/S2215098618312345

Relative Articles

Supplements(0)

Cited By

Proportional views

Proportional views

Figures(9) / Tables(2)

Get Citation

PDF

XML

Article Metrics

Article views(157) PDF downloads(0)

Research on multi-feature human pose model recognition based on one-shot learning

doi: 10.12086/oee.2021.200099

Abstract

References

Proportional views

Catalog

Article Metrics

Proportional views

Related

Research on multi-feature human pose model recognition based on one-shot learning

doi: 10.12086/oee.2021.200099

Abstract

References

Proportional views

Catalog

Article Metrics

Proportional views

Related

Export File

Citation

Format

Content