Human body joint nodes detection based on DeepPose and Faster RCNN

doi:10.7523/j.issn.2095-6134.2020.06.015

›› 2020, Vol. 37 ›› Issue (6): 828-834.DOI: 10.7523/j.issn.2095-6134.2020.06.015

• Research Articles • Previous Articles Next Articles

Human body joint nodes detection based on DeepPose and Faster RCNN

YU Baoling¹, YU Songkun¹, SUN Yaoran², YANG Zhen³, FU Xubo¹

1. Department of Public Physical and Art Education, Zhejiang University, Hangzhou 310058, China;
2. College of Optical Science and Engineering, Zhejiang University, Hangzhou 310058, China;
3. Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China

Received:2019-05-07 Revised:2019-10-30 Online:2020-11-15

Abstract

Abstract: Human body joint nodes detection is a considerably challenging task which has drawn enormous attention in the field of computer vision recently. The challenges of this task include: coping with the complex structure of human body joints, denoting the interdependence between joint nodes, and dealing with the sheltered and overlapped body joint nodes. Among the common solutions to this task, the models based on deep learning are widely applied and provide useful results. However, the existing models have following drawbacks: 1) comparatively low accuracy in prediction; 2) poor performance in multi-objective tasks. In our work, we proposed a novel method aiming at more satisfactory results. We firstly detect the relevant regions of human body with Faster RCNN, and then input the regions into a modified DeepPose algorithm. We achieve the state-of-the-art results in the detection of the wrist and knee on MPII dataset, improving 1.2% and 0.3% in PCKh, respectively. The total PCKh is 87.6% on MPII dataset.

Key words: Faster RCNN, DeepPose, human body joint nodes detection

CLC Number:

TP212.9

YU Baoling, YU Songkun, SUN Yaoran, YANG Zhen, FU Xubo. Human body joint nodes detection based on DeepPose and Faster RCNN[J]. , 2020, 37(6): 828-834.

References

[1] Tompson J J, Jain A, LeCun Y, et al. Joint training of a convolutional network and a graphical model for human pose estimation[C]//Advances in Neural Information Processing Systems. 2014:1799-1807.
[2] Toshev A, Szegedy C, DeepPose G. Human pose estimation via deep neural networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, OH, USA. 2014:24-27.
[3] Sapp B, Taskar B. Modec:multimodal decomposable models for human pose estimation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2013:3674-3681.
[4] Gkioxari G, Toshev A, Jaitly N. Chained predictions using convolutional neural networks[C]//European Conference on Computer Vision. Springer, Cham, 2016:728-743.
[5] He K, Zhang X, Ren S, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[J]. IEEE Transactions On Pattern Analysis and Machine Intelligence, 2015, 37(9):1904-1916.
[6] Girshick R. Fast r-cnn[C]//Proceedings of the IEEE International Conference on Computer Vision. 2015:1440-1448.
[7] Gkioxari G, Girshick R, Malik J. Contextual action recognition with r* cnn[C]//Proceedings of the IEEE International Conference on Computer Vision. 2015:1080-1088.
[8] He K, Gkioxari G, Dollár P, et al. Mask r-cnn[C]//Proceedings of the IEEE International Conference on Computer Vision. 2017:2961-2969.
[9] Ren S, He K, Girshick R, et al. Faster r-cnn:Towards real-time object detection with region proposal networks[C]//Advances in Neural Information Processing Systems. 2015:91-99.
[10] Everingham M, Van Gool L, Williams C K I, et al. The pascal visual object classes (voc) challenge[J]. International Journal of Computer Vision, 2010, 88(2):303-338.
[11] Johnson S, Everingham M. Clustered pose and nonlinear appearance models for human pose estimation[C]//BMVC 2010. doi:10.5244/c.24.12.
[12] MPI human pose dafaset[DB/OL].[2019-05-05]. http://human-pose.mpi-inf.mpg.de/#evaluation.
[13] Tompson J, Goroshin R, Jain A, et al. Efficient object localization using convolutional networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015:648-656.
[14] Pishchulin L, Insafutdinov E, Tang S, et al. Deepcut:joint subset partition and labeling for multi person pose estimation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016:4929-4937.
[15] Wei S E, Ramakrishna V, Kanade T, et al. Convolutional pose machines[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016:4724-4732.

Human body joint nodes detection based on DeepPose and Faster RCNN

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 3

Recommended Articles

Metrics

Comments

[1]	ZHU Shikun, LI Dong, HUANG Kui, ZHANG Baoxian. Design and realization of an efficient light-weighted file system for wireless sensor networks [J]. Journal of University of Chinese Academy of Sciences, 2014, 31(1): 130-134.
[2]	LI Li-Juan, ZHAO Tong. Cross-layer energy consumption optimal model and its solution algorithm in wireless sensor networks [J]. , 2011, 28(3): 375-381.
[3]	JIANG Zhi-Peng, GAO Sui-Xiang. Localization algorithm without distance information for wireless sensor networks [J]. , 2011, 28(3): 382-388.