Abstract
The analysis of human actions based on 3D skeleton data becomes popular recently due to its succinctness, robustness, and view-invariant representation. Recent attempts on this problem suggested to use human body affinity fields to efficiently detect the 2D pose of multiple people in an image. In this paper, we extend this idea to 3D domains and develop a 3D human action recognition system with ability to understand the cooperative action of several people. To achieve this, we firstly extract the human body affinity fields to robustly represent associate 2D human skeleton with individuals in the image. Inspired by the triangulation techniques in stereo vision analysis, 3D human skeleton data can be obtained. To handle the noise in constructed 3D human skeleton data, we introduce an enhanced light-weight matching algorithm based on Dynamic Time Warping (DTW) to compute the matching cost. The real-life experiments demonstrate the efficiency and applicability of our approach.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Wang, H., Schmid, C.: Action recognition with improved trajectories. In: Proceedings of IEEE International Conference Computer Vision, pp. 3551–3558 (2013)
Wang, H., Kläser, A., Schmid, C., Liu, C.L.: Dense trajectories and motion boundary descriptors for action recognition. Int. J. Comput. Vis. 103(1), 60–79 (2013)
Geng-Dai, L., Ming-Liang, X., Ming-min, Z.: Human motion synthesis based on independent spatio-temporal feature space. Chinese J. Comput. 34(3), 464–472 (2011)
Tao, Q., Dexiang, D., Hui, L., Lian, Z., Yifeng, L.: Extracting spatio-temporal features via multi-layer independent subspace analysis for action recognition. Geomatics Inf. Sci. WuHan Univ. 41(4) (2016)
Zhang, S., Liu, X., Xiao, J.: On geometric features for skeleton-based action recognition using multilayer LSTM networks. In: Proceedings of 2017 IEEE Winter Conference on Applications of Computer Vision, WACV 2017, pp. 148–157 (2017)
Liu, J., Shahroudy, A., Xu, D., Wang, G.: Spatio-temporal LSTM with trust gates for 3D human action recognition. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9907, pp. 816–833. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46487-9_50
Chen, G., Giuliani, M., Clarke, D., Gaschler, A., Knoll, A.: Action recognition using ensemble weighted multi-instance learning. In: Proceedings of IEEE International Conference on Robotics and Automation, pp. 4520–4525 (2014)
Zhu, W., et al.: Co-occurrence feature learning for skeleton based action recognition using regularized deep LSTM networks, no. 2, pp. 3697–3703 (2016)
Guohui, T., Jianqin, Y., Xu, H., Jing, Y.: A novel human activity recognition method using joint points information. Robot 36(3), 285–292 (2014)
Sempena, S., Maulidevi, N.U., Aryan, P.R.: Human action recognition using dynamic time warping. In: Proceedings of 2011 International Conference on Electrical Engineering Informatics, no. July, pp. 1–5 (2011)
Cao, Z., Simon, T., Wei, S.-E., Sheikh, Y.: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields (2016)
Galna, B., Barry, G., Jackson, D., Mhiripiri, D., Olivier, P., Rochester, L.: Accuracy of the Microsoft Kinect sensor for measuring movement in people with Parkinson’s disease. Gait Posture 39(4), 1062–1068 (2014)
Chengfeng, W., Hong, C., Ruixuan, Z., Dehai, Z., Qing, W., Shuli, M.: Research on DTW action recognition algorithm with joint weighting. J. Graph. 37(4) (2016)
Liu, Z.: Zicheng Liu at Microsoft Research (2017). https://www.microsoft.com/en-us/research/people/zliu/#!projects
ZED Stereo Camera: https://www.stereolabs.com
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Dong, H., Meng, Q., Hu, T. (2018). A Novel 3D Human Action Recognition Method Based on Part Affinity Fields. In: Bi, Y., Chen, G., Deng, Q., Wang, Y. (eds) Embedded Systems Technology. ESTC 2017. Communications in Computer and Information Science, vol 857. Springer, Singapore. https://doi.org/10.1007/978-981-13-1026-3_14
Download citation
DOI: https://doi.org/10.1007/978-981-13-1026-3_14
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-1025-6
Online ISBN: 978-981-13-1026-3
eBook Packages: Computer ScienceComputer Science (R0)