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A Joint Hierarchy Model for Action Recognition Using Kinect

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Artificial Intelligence and Robotics

Part of the book series: Studies in Computational Intelligence ((SCI,volume 752))

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Abstract

In this paper, we proposed a joint hierarchy model to represent the motion of human according to the covariance feature of adjacent joints using Kinect. SVM is used for the action classification. Experimental results show that the proposed model improves the recognition accuracy with less computation complexity.

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References

  1. Lu, H., Li, Y.,. Chen, M., Kim, H., Serikawa, S.: Brain intelligence: go beyond artificial intelligence. Int. J. Comput. Sci. Comput Vis. Patt. Recogn. eprint arXiv:1706.01040 (2017). https://arxiv.org/abs/1706.01040

  2. Lu, H., Li, B., Zhu, J., Li, Y., Xu, X., He, L., Li, X., Li, J., Serikawa, S.: Wound intensity correction and segmentation with convolutional neural networks. J. Concurrency Comput: Pract. Experience 29(6) (2017). http://onlinelibrary.wiley.com/doi/10.1002/cpe.v29.6/issuetoc

  3. Lu, H., Li, Y., Uemura, T., Ge, Z., Xu, X., He, L., Serikawa, S.: FDCNet: filtering deep convolutional network for marine organism classification. J. Multimedia Tools Appl. 1–14 (2017)

    Google Scholar 

  4. Blank, M., Gorelick, L., Shechtman, E. et al.: Actions as space-time shapes. Comput. Vis. ICCV 2005. In: Tenth IEEE International Conference on IEEE, vol. 2, pp. 1395–1402 (2005)

    Google Scholar 

  5. Xia, L., Chen, C.C., Aggarwal, J.K.: View invariant human action recognition using histograms of 3D joints. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 20–27. IEEE (2012)

    Google Scholar 

  6. Li, W., Zhang, Z., Liu, Z.: Action recognition based on a bag of 3D points. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition-Workshops, pp. 9–14. IEEE (2010)

    Google Scholar 

  7. Yang, X., Tian, Y.L.: EigenJoints-based action recognition using naïve-bayes-nearest-neighbor 38(3c), 14–19 (2012)

    Google Scholar 

  8. Yuan, M., Chen, E., Gao, L.: Posture selection based on two-layer AP with application to human action recognition using HMM. In: IEEE International Symposium on Multimedia, pp. 359–364. IEEE Computer Society (2016)

    Google Scholar 

  9. Lu, G., Zhou, Y., Li, X., et al.: Efficient action recognition via local position offset of 3D skeletal body joints. Multimedia Tools Appl 75(6), 3479–3494 (2016)

    Article  Google Scholar 

  10. Zhou, L., Li, W., Zhang, Y., et al.: Discriminative key pose extraction using extended LC-KSVD for action recognition. In: International Conference on Digital Image Computing: Techniques and Applications, pp. 1–8. IEEE (2014)

    Google Scholar 

  11. Vemulapalli, R., Arrate. F., Chellappa, R.: Human action recognition by representing 3D skeletons as points in a Lie group. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 588–595. IEEE Computer Society (2014)

    Google Scholar 

  12. Li, J., Chen, J., Sun, L.: Joint motion similarity (JMS)-based human action recognition using kinect. In: International Conference Digital Image Computing: Techniques and Applications (DICTA), pp. 1–8. IEEE (2016)

    Google Scholar 

  13. Zhu, Y., Chen, W., Guo, G.: Fusing spatiotemporal features and joints for 3D action recognition. In: Computer Vision and Pattern Recognition Workshops, pp. 486–491. IEEE (2013)

    Google Scholar 

  14. Hussein, M.E., Torki, M., Gowayyed, M.A., et al.: Human action recognition using a temporal hierarchy of covariance descriptors on 3D joint locations. In: Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, pp. 2462–2472 (2013)

    Google Scholar 

  15. Fothergill, S., Mentis, H., Kohli, P., et al.: Instructing people for training gestural interactive systems. In: Sigchi Conference on Human Factors in Computing Systems, pp. 1737–1746. ACM (2012)

    Google Scholar 

  16. Li, W., Zhang, Z., Liu, Z.: Action recognition based on a bag of 3D points. In: Computer Vision and Pattern Recognition Workshops, pp. 9–14. IEEE (2010)

    Google Scholar 

  17. Shotton, J., Fitzgibbon, A.W., Cook, M., Sharp, T., Finocchio, M., Moore, R., Kipman, A., Blake, A.: Real-time human pose recognition in parts from single depth images. In: CVPR, pp. 1297–1304 (2011)

    Google Scholar 

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Acknowledgements

This work was supported by the open research fund of Key Lab of Broadband Wireless Communication and Sensor Network Technology (Nanjing University of Posts and Telecommunications, Ministry of Education, NY217025), funding from Nanjing University of Posts and Telecommunications(NY217021), National Natural Science Foundation of China (Grant No. 61401228), China Postdoctoral Science Foundation (Grant No. 2015M581841), and Postdoctoral Science Foundation of Jiangsu Province (Grant No. 1501019A).

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Authors and Affiliations

  1. Key Lab of Broadband Wireless Communication and Sensor Network Technology, Nanjing University of Posts and Telecommunications, Ministry of Education, Nanjing, NY217025, China

    Qicheng Pei, Jianxin Chen, Lizheng Liu & Chenxuan Xi

Authors
  1. Qicheng Pei
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  2. Jianxin Chen
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  3. Lizheng Liu
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  4. Chenxuan Xi
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Corresponding authors

Correspondence to Qicheng Pei or Jianxin Chen .

Editor information

Editors and Affiliations

  1. Department of Mechanical and Control Engineering, Kyushu Institute of Technology, Kitakyushu, Japan

    Huimin Lu

  2. School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China

    Xing Xu

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Pei, Q., Chen, J., Liu, L., Xi, C. (2018). A Joint Hierarchy Model for Action Recognition Using Kinect. In: Lu, H., Xu, X. (eds) Artificial Intelligence and Robotics. Studies in Computational Intelligence, vol 752. Springer, Cham. https://doi.org/10.1007/978-3-319-69877-9_8

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  • DOI: https://doi.org/10.1007/978-3-319-69877-9_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-69876-2

  • Online ISBN: 978-3-319-69877-9

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