Human Motion Recognition from 3D Pose Information - Trisarea: A New Pose-based Feature

M. Vinagre, J. Aranda, A. Casals

Abstract

The use of pose-based features has demonstrated to be a promising approach for human motion recognition. Encouraged by the results achieved, a new relational pose-based feature, Trisarea, based on geometric relationship between human joints, is proposed and analysed. This feature is defined as the area of the triangle formed by connecting three joints. The paper shows how the variation of a selected set of Trisarea features over time constitutes a descriptor of human motion. It also demonstrates how this motion descriptor based on Trisarea features can provide useful information in terms of human motion for its application to action recognition tasks.

References

  1. Aggarwal, J. and Ryoo, M. (2011). Human activity analysis: A review. ACM Computing Survey, 43(3):16:1-16:43.
  2. Chen, C., Zhuang, Y., Xiao, J., and Liang, Z. (2009). Perceptual 3D pose distance estimation by boosting relational geometric features. Computer Animation and Virtual Worlds, 20(2-3):267277.
  3. Ellis, C., Masood, S. Z., Tappen, M. F., Laviola, Jr., J. J., and Sukthankar, R. (2013). Exploring the trade-off between accuracy and observational latency in action recognition. International Journal of Computer Vision, 101(3):420-436.
  4. Gorelick, L., Blank, M., Shechtman, E., Irani, M., and Basri, R. (2007). Actions as space-time shapes. Transactions on Pattern Analysis and Machine Intelligence, 29(12):2247-2253.
  5. Gu, J., Ding, X., Wang, S., and Wu, Y. (2010). Action and gait recognition from recovered 3-d human joints. Trans. Sys. Man Cyber. Part B, 40(4):1021-1033.
  6. Li, W., Zhang, Z., and Liu, Z. (2010). Action recognition based on a bag of 3d points. In Computer Vision and Pattern Recognition Workshops (CVPRW), 2010 IEEE Computer Society Conference on, pages 9-14, Washington, DC, USA. IEEE Computer Society.
  7. Lu, Y., Cohen, I., Zhou, X. S., and Tian, Q. (2007). Feature selection using principal feature analysis. In Proceedings of the 15th international conference on Multimedia, pages 301-304, New York, NY, USA. ACM.
  8. Luo, X., Berendsen, B., Tan, R. T., and Veltkamp, R. C. (2010). Human pose estimation for multiple persons based on volume reconstruction. In Proceedings of the 2010 20th International Conference on Pattern Recognition, pages 3591-3594, Washington, DC, USA. IEEE Computer Society.
  9. Matikainen, P., Hebert, M., and Sukthankar, R. (2010). Representing pairwise spatial and temporal relations for action recognition. In Proceedings of the 11th European conference on Computer vision: Part I, pages 508-521, Berlin, Heidelberg. Springer-Verlag.
  10. Matthias Straka, Stefan Hauswiesner, M. R. and Bischof, H. (2011). Skeletal graph based human pose estimation in real-time. In Proceedings of the British Machine Vision Conference, pages 69.1-69.12, Aberystwyth, Wales. BMVA Press.
  11. Miranda, L., Vieira, T., Morera, D. M., Lewiner, T., Vieira, A. W., and Campos, M. F. M. (2012). Real-time gesture recognition from depth data through key poses learning and decision forests. In SIBGRAPI, pages 268-275, Washington, DC, USA. IEEE Computer Society.
  12. Müller, M., Baak, A., and Seidel, H.-P. (2009). Efficient and robust annotation of motion capture data. In Proceedings of the 2009 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, pages 17-26, New York, NY, USA. ACM.
  13. Poppe, R. (2010). A survey on vision-based human action recognition. Image and Vision Computing, 28(6):976 - 990.
  14. Raptis, M., Kirovski, D., and Hoppe, H. (2011). Real-time classification of dance gestures from skeleton animation. In Proceedings of the 2011 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, pages 147-156, New York, NY, USA. ACM.
  15. Schwarz, L., Mateus, D., Castaneda, V., and Navab, N. (2010). Manifold learning for tof-based human body tracking and activity recognition. In Proceedings of the British Machine Vision Conference, pages 80.1-80.11, Aberystwyth, Wales. BMVA Press.
  16. Shotton, J., Fitzgibbon, A., Cook, M., Sharp, T., Finocchio, M., Moore, R., Kipman, A., and Blake, A. (2011). Real-time human pose recognition in parts from single depth images. In Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition, pages 1297-1304, Washington, DC, USA. IEEE Computer Society.
  17. Sung, J., Ponce, C., Selman, B., and Saxena, A. (2012). Unstructured human activity detection from rgbd images. In Robotics and Automation (ICRA), 2012 IEEE International Conference on, pages 842-849, Washington, DC, USA. IEEE.
  18. Uddin, M. Z., Thang, N. D., Kim, J. T., and Kim, T.-S. (2011). Human activity recognition using body joint-angle features and hidden markov model. ETRI Journal, 33(4):569-579.
  19. Vieira, A. W., Nascimento, E. R., Oliveira, G. L., Liu, Z., and Campos, M. F. M. (2012). Stop: Space-time occupancy patterns for 3d action recognition from depth map sequences. In Proceedings of the 17th Iberoamerican Congress, pages 252-259, Berlin, Heidelberg. Springer-Verlag.
  20. Wang, J., Liu, Z., Wu, Y., and Yuan, J. (2012). Mining actionlet ensemble for action recognition with depth cameras. In Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 1290-1297, Washington, DC, USA. IEEE Computer Society.
  21. Xia, L., Chen, C.-C., and Aggarwal, J. K. (2012). View invariant human action recognition using histograms of 3d joints. In CVPR Workshops, pages 20-27, Washington, DC, USA. IEEE.
  22. Yao, A., Gall, J., Fanelli, G., and Van Gool, L. (2011). Does human action recognition benefit from pose estimation? In Proceedings of the British Machine Vision Conference, pages 67.1-67.11, Aberystwyth, Wales. BMVA Press.
  23. Yun, K., Honorio, J., Chattopadhyay, D., Berg, T., and Samaras, D. (2012). Two-person interaction detection using body-pose features and multiple instance learning. In Computer Vision and Pattern Recognition Workshops (CVPRW), 2012 IEEE Computer Society Conference on, pages 28-35. IEEE.
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Paper Citation


in Harvard Style

Vinagre M., Aranda J. and Casals A. (2013). Human Motion Recognition from 3D Pose Information - Trisarea: A New Pose-based Feature . In Proceedings of the 10th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO, ISBN 978-989-8565-71-6, pages 74-82. DOI: 10.5220/0004482800740082


in Bibtex Style

@conference{icinco13,
author={M. Vinagre and J. Aranda and A. Casals},
title={Human Motion Recognition from 3D Pose Information - Trisarea: A New Pose-based Feature},
booktitle={Proceedings of the 10th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,},
year={2013},
pages={74-82},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004482800740082},
isbn={978-989-8565-71-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,
TI - Human Motion Recognition from 3D Pose Information - Trisarea: A New Pose-based Feature
SN - 978-989-8565-71-6
AU - Vinagre M.
AU - Aranda J.
AU - Casals A.
PY - 2013
SP - 74
EP - 82
DO - 10.5220/0004482800740082