Abstract
In this paper, we propose the upper body pose estimation algorithm using 3-dimensional model and depth image. The conventional ICP algorithm is modified by adding visibility estimation and key points - extreme points and elbow locations. The visibility estimation keeps occluded points from participating in pose estimation to alleviate the affection of self-occlusion problem. Introduction of extreme points and elbow locations, which are extracted using geodesic distance map and particle filter, improves the accuracy of pose estimation result. The optimal parameters of the model are obtained from nonlinear mathematical optimization solver. The experimental results show that the proposed method accurately estimates the various human poses with self-occlusion.
Chapter PDF
References
Shotton, J., Girshick, R., Fitzgibbon, A., Sharp, T., Cook, M., Finocchio, M., Moore, R., Kohli, P., Criminisi, A., Kipman, A., Blake, A.: Efficient human pose estimation from single depth images. IEEE Transactions on Pattern Analysis and Machine Intelligence PP(99), 1 (2012)
Breiman, L.: Random forests. Machine Learning 45(1), 5–32 (2001)
Hernandez-Vela, A., Zlateva, N., Marinov, A., Reyes, M., Radeva, P., Dimov, D., Escalera, S.: Graph cuts optimization for multi-limb human segmentation in depth maps. In: IEEE Conference onComputer Vision and Pattern Recognition, pp. 726–732 (2012)
Grest, D., Woetzel, J., Koch, R.: Nonlinear body pose estimation from depth images. In: Kropatsch, W.G., Sablatnig, R., Hanbury, A. (eds.) DAGM 2005. LNCS, vol. 3663, pp. 285–292. Springer, Heidelberg (2005)
Plagemann, C., Ganapathi, V., Koller, D., Thrun, S.: Real-time identification and localization of body parts from depth images. In: IEEE International Conference on Robotics and Automation, pp. 3108–3113 (2010)
Schwarz, L.A., Mkhitaryan, A., Mateus, D., Navab, N.: Human skeleton tracking from depth data using geodesic distances and optical flow. Image and Vision Computing 30(3), 217–226 (2012)
Besl, P.J., McKay, H.D.: A method for registration of 3-D shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence 14(2), 239–256 (1992)
Siddiqui, M.: Human pose estimation from a single view point. PhD thesis, University of Southern California, Adviser - Gerard Medioni (2009)
Knoop, S., Vacek, S., Dillmann, R.: Modeling joint constraints for an articulated 3D human body model with artificial correspondences in ICP. In: IEEE-RAS International Conference on Humanoid Robots, pp. 74–79 (2005)
Kim, D., Kim, D.: A novel fitting algorithm using the ICP and the particle filters for robust 3D human body motion tracking (2008)
Droeschel, D., Behnke, S.: 3D body pose estimation using an adaptive person model for articulated ICP. In: Jeschke, S., Liu, H., Schilberg, D. (eds.) ICIRA 2011, Part II. LNCS, vol. 7102, pp. 157–167. Springer, Heidelberg (2011)
Haug, A.: A tutorial on bayesian estimation and tracking techniques applicable to nonlinear and non-gaussian processes. MITRE Corporation, McLean (2005)
Chong, E.K., Zak, S.H.: An introduction to optimization. Wiley-interscience (2004)
Trottenberg, U., Oosterlee, C.W., Schüller, A.: Multigrid. Academic Pr. (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Huh, S., Kim, G. (2013). Human Pose Estimation from Depth Image Using Visibility Estimation and Key Points. In: Duffy, V.G. (eds) Digital Human Modeling and Applications in Health, Safety, Ergonomics, and Risk Management. Human Body Modeling and Ergonomics. DHM 2013. Lecture Notes in Computer Science, vol 8026. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39182-8_39
Download citation
DOI: https://doi.org/10.1007/978-3-642-39182-8_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39181-1
Online ISBN: 978-3-642-39182-8
eBook Packages: Computer ScienceComputer Science (R0)