Abstract
This paper proposes an approach to classify human arm motion using qualitative normalized templates. The proposed method consists of construction of human arm model, qualitative representation of prior knowledge of human arm motion and a search algorithm. First, convention robotic model is employed to build up a generic vision model for a human arm; Secondly, qualitative robotic model in [1] is used to construct qualitative normalised templates; Finally a search algorithm is provided to match the vision model with the templates in image frames. Experimental evaluation demonstrates that the proposed method is effective for the classification of human-arm motion. Future work will focus on extending the proposed method to the classification of a full human-body motion.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Liu, H., Coghill, G., Xu, H.: Qualitative modelling of kinematic robots for fault diagnosis. International Journal of Production Research 43(11), 2277–2290 (2005)
Guo, Y., Xu, G., Tsuji, S.: Understanding human motion patterns. In: Proceedings of the 12th IAPR International Conference on Pattern Recognition, vol. 2, pp. 325–329 (1994)
Yamamoto, M., Koshikawa, K.: Human motion analysis based on a robot arm model. In: CVPR 1991: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 664–665 (1991)
Ju, S.X., Black, M.J., Yacoob, Y.: Cardboard people: A parameterized model of articulated image motion. In: FG 1996: Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG 1996), p. 38. IEEE Computer Society Press, Washington (1996)
Kakadiaris, I., Metaxas, D.: Model-based estimation of 3d human motion. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(12), 1453–1459 (2000)
Tissainayagam, P., Suter, D.: Contour tracking with automatic motion model switching. Pattern Recognition Society 36, 2411–2427 (2003)
Bregler, C., Malik, J.: Tracking people with twists and exponential maps. In: CVPR 1998: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, p. 8. IEEE Computer Society Press, Washington (1998)
Bissacco, A.: Visual tracking of human body with deforming motion and shape average. Technical report, UCLA (2002)
Lee, M.W., Cohen, I., Jung, S.K.: Particle filter with analytical inference for human body tracking. In: MOTION 2002: Proceedings of the Workshop on Motion and Video Computing, p. 159. IEEE Computer Society Press, Washington (2002)
Yonemoto, S., Arita, D., Taniguchi, R.: Real-time human motion analysis and ik-based human figure control. In: HUMO 2000: Proceedings of the Workshop on Human Motion (HUMO 2000), p. 149. IEEE Computer Society Press, Washington (2000)
Sundaresan, A., Chellappa, R., RoyChowdhury, A.: Multiple view tracking of humans modelled by kinematic chains. In: International Conference on Image Processing (2004)
Liu, H., Coghill, G.M.: Fuzzy qualitative trigonometry. In: IEEE International Conference on Systems, Man and Cybernetics (2005)
Murray, R.M., Shastry, S., Li, Z., Sastry, S.S.: A Mathematical Introduction to Robotic Manipulation. CRC Press, Boca Raton (1994)
Craig, J.: Introduction to robotics: Mechanics and control, 2nd edn. Addison-Wesley, Reading (1989)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chan, C.S., Liu, H., Brown, D.J. (2006). Human Arm-Motion Classification Using Qualitative Normalised Templates. In: Gabrys, B., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2006. Lecture Notes in Computer Science(), vol 4251. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11892960_77
Download citation
DOI: https://doi.org/10.1007/11892960_77
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46535-5
Online ISBN: 978-3-540-46536-2
eBook Packages: Computer ScienceComputer Science (R0)