Skip to main content
SpringerLink
Log in

Human body motion parameters capturing using kinect

  • Original Paper
  • Published:
Machine Vision and Applications Aims and scope Submit manuscript

Abstract

This paper introduces a new real-time human motion parameters capturing method using Kinect. It consists of five modules. First, the hybrid action type classifier categories human body motion into four different action types. Second, for each action type, there is a body part (BP) classifier which segments the human silhouette into 16 BP regions of which the centroids become the BP joints. These BP joints are linked to represent the human body skeleton. Third, an action type validation process verifies the identified action type. Fourth, the partial occlusion recovery method relocates the occluded BP joints. Fifth, the offset compensation process fine tunes the positions of BP joints and then validates the compensation results. The major contributions of this paper are hybrid action type classification and correction, offset compensation, and partial occlusion recovery. The experimental results show that this method can estimate human upper limb motion parameters in real time accurately and effectively.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

References

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

  2. Ganapathi, V., Plagemann, C., Koller, D., Thrun, S.: Real time motion capture using a single time-of-flight camera. In: IEEE CVPR (2010)

  3. Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. IEEE Trans. PAMI 24(3), 509–522 (2002)

    Article  Google Scholar 

  4. Baak, A., Müller, M., Bharaj, G., Seidel, H.-P., Theobalt, H.: A data-driven approach for real-time full body pose reconstruction from a depth camera. In: ICCV (2011)

  5. Hara, K.: Real-time inference of 3D human poses by assembling local patches. In: WACV (2009)

  6. Moeslund, T., Hilton, A., Krüger, V.: A survey of advances in vision-based human motion capture and analysis. In: CVIU (2006)

  7. Poppe, R.: Vision-based human motion analysis: an overview. In: CVIU, p 108 (2007)

  8. Microsoft Xbox 360 Kinect Launches November 4, Microsoft Corp. Redmond WA (2010)

  9. Grest, D., Woetzel, J., Koch, R.: Nonlinear body pose estimation from depth images. In: Proceedings of DAGM (2005)

  10. Knoop, S., Vacek, S., Dillmann, R.: Sensor fusion for 3D human body tracking with an articulated 3D body model. in: Proceedings of ICRA (2006)

  11. Zhu, Y., Fujimura, K.: Constrained optimization for human pose estimation from depth sequences. In: Proceedings of ACCV (2007)

  12. Siddiqui, M., Medioni, G.: Human pose estimation from a single view point, real-time range sensor. In: CVCG at CVPR (2010)

  13. Plagemann, C., Ganapathi, V., Koller, D., Thrun, S.: Real-time identification and localization of BPs from depth images. In: Proceedings of ICRA (2010)

  14. Mori, G., Malik, J.: Estimating human body configurations using shape context matching. In: IEEE ICCV (2003)

  15. Bourdev, L., Malik, J.: Poselets: BP detectors trained using 3D human pose annotations. In: ICCV (2009)

  16. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)

    Article  MATH  Google Scholar 

  17. Lepetit, V., Lagger, P., Fua, P.: Randomized trees for real-time keypoint recognition. In: CVPR (2005)

  18. Shotton, J., Johnson, M., Cipolla, R.: Semantic texton forests for image categorization and segmentation. In: IEEE CVPR (2008)

  19. Wang, R., Popovi’c, J.: Real-time hand-tracking with a color glove. In: Proceedings of ACM SIGGRAPH (2009)

  20. Bregler, C., Malik, J.: Tracking people with twists and exponential maps. In: Proceedings of CVPR (1998)

  21. Sigal, L., Bhatia, S., Roth, S., Black, M., Isard, M.: Tracking looselimbed people. In: IEEE CVPR (2004)

  22. Grest, D., Woetzel, J., Koch, R.: Nonlinear body pose estimation from depth images. In: Proceedings of DAGM (2005)

  23. Sharp, T.: Implementing decision trees and forests on a GPU. In: Procedings of ECCV (2008)

  24. Ho, T.K.: Random decision forest. In: Proceedings of the 3rd ICDAR, Montreal, QC, 14–16 August 1995. pp. 278–282 (1995)

  25. Morio, G., Malik, J.: Estimating human body configuration using shape context matching. In: ECCV (2002)

  26. Athitsos, V., Sclaroff, S.: Estimating 3D hand pose from cluttered image. In: IEEE CVPR (2003)

  27. Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. IEEE Trans. PAMI. 24(4) (2002)

  28. Babich, G., Camps, O.: Weighted Parzen windows for pattern classification. IEEE Trans. PAMI 18(5) (1996)

  29. Gionis, A., Indyk, P., Motwani, R.: Similarity search in high dimensions via hashing. In: 25th International Conference on Very Large Data Base (1999)

  30. Shakhnarovich, G., Viola, P., Darrell, T.: Fast pose estimation with parameter sensitive hashing. In: Proceedings of ICCV (2003)

  31. Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. IEEE Trans. PAMI 24(4), 603–619 (2002)

    Article  Google Scholar 

  32. Adams, R., Bischof, L.: Seeded region growing. IEEE Trans. PAMI 16(5) (1994)

  33. Shen, W., Deng, K., Bai, X.: Exemplar-based human action pose correction and tagging. In: IEEE CVPR (2012)

  34. Hsu, S.-C., Huang, J.-Y., Huang, C.-L.: Human upper body posture recognition and upper limbs motion parameters estimation. In: APSIPA ASC, Kaohsiung, Taiwan (2013)

  35. Wang, R., Paris, S., Popovic, J.: Practical color-based motion capture. In: Proceedings of 21th ACM SIGGRAPH/ Eurographics Symposium on Computer Animation (2011)

  36. Freund, Y., Schapire, R.E.: a decision-theoretic generalization of on-line learning and an application to boosting. JCSS 55, 119–139 (1997)

    MATH  MathSciNet  Google Scholar 

  37. Oreifej, O., Liu, Z.: HON4D: Histogram of oriented 4D normal for activity recognition from depth sequences. In: IEEE CVPR (2013)

Download references

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, National Tsing-Hua University, Hsin-Chu, Taiwan

    Shih-Chung Hsu, Jun-Yang Huang & Wei-Chia Kao

  2. Department of Applied Informatics and Multimedia, Asia University, Tai-Chung, Taiwan

    Chung-Lin Huang

Authors
  1. Shih-Chung Hsu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jun-Yang Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wei-Chia Kao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chung-Lin Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chung-Lin Huang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hsu, SC., Huang, JY., Kao, WC. et al. Human body motion parameters capturing using kinect. Machine Vision and Applications 26, 919–932 (2015). https://doi.org/10.1007/s00138-015-0710-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00138-015-0710-1

Keywords

Search

Navigation