Abstract
Accurate human posture estimation from single or multiple images is essential in many applications. Two main causes of difficulty to solve the estimation problem are large number of degrees of freedom and self-occlusion. Tree-structured graphical models with efficient inference algorithms have been used to solve the problem in a lower dimensional state space. However, such models are not accurate enough to formulate the problem because it assumes that the image of each body part can be independently observed. As a result, it is difficult to handle partial self-occlusion. This paper presents a more accurate graphical model which can implicitly model the possible self-occlusion between body parts. More important, an efficiently approximate inference algorithm is provided to estimate human posture in a low dimensional state space. It can deal with partial self-occlusion in posture estimation and human tracking, which has been shown by the experimental results on real data.
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
Agarwal, A., Triggs, B.: 3d human pose from silhouettes by relevance vector regression. In: Proc. IEEE Conf. on CVPR, pp. 882–888 (2004)
Athitsos, V., Alon, J., Sclaroff, S., Kollios, G.: Boostmap: A method for efficient approximate similarity rankings. In: Proc. IEEE Conf. on CVPR, pp. 268–275 (2004)
Cham, T., Rehg, J.: A multiple hypothesis approach to figure tracking. In: Proc. IEEE Conf. on CVPR, pp. 239–245 (1999)
Comaniciu, D., Meer, P.: Mean shift: A robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Machine Intell. 24, 603–619 (2002)
Deutscher, J., Blake, A., Reid, I.: Articulated body motion capture by annealed particle filtering. In: Proc. IEEE Conf. on CVPR, pp. 126–133 (2000)
Elgammal, A., Lee, C.: Inferring 3d body pose from silhouettes using activity manifold learning. In: Proc. IEEE Conf. on CVPR, pp. 681–688 (2004)
Felzenszwalb, P.F., Huttenlocher, D.P.: Pictorial structures for object recognition. Int. Journal of Computer Vision 61(1), 55–79 (2005)
Gavrila, D.M.: The visual analysis of human movement: A survey. Computer Vision and Image Understanding: CVIU 73(1), 82–98 (1999)
Hua, G., Yang, M.H., Wu, Y.: Learning to estimate human pose with data driven belief propagation. In: Proc. IEEE Conf. on CVPR, pp. 747–754 (2005)
Ioffe, S., Forsyth, D.: Finding people by sampling. In: Proc. IEEE Conf. on ICCV, pp. 1092–1097 (1999)
Isard, M.: Pampas: Real-valued graphical models for computer vision. In: Proc. IEEE Conf. on CVPR, pp. 613–620 (2003)
Isard, M., Blake, A.: Contour tracking by stochastic propagation of conditional density. In: Buxton, B.F., Cipolla, R. (eds.) ECCV 1996. LNCS, vol. 1064, pp. 343–356. Springer, Heidelberg (1996)
Lan, X., Huttenlocher, D.P.: Beyond trees: common-factor models for 2D human pose recovery. In: Proc. IEEE Conf. on ICCV, pp. 470–477 (2005)
Lee, M.W., Cohen, I.: Proposal maps driven mcmc for estimating human body pose in static images. In: Proc. IEEE Conf. on CVPR, pp. 334–341 (2004)
Mori, G.: Guiding model search using segmentation. In: Proc. IEEE Conf. on ICCV (2005)
Mori, G., Malik, J.: Estimating human body configurations using shape context matching. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2352, pp. 666–680. Springer, Heidelberg (2002)
Urtasun, A.H.R., Fleet, D.J., Fua, P.: Priors for people tracking from small training sets. In: IEEE Int. Conf. ICCV (2005)
Ramanan, D., Forsyth, D.A., Zisserman, A.: Strike a pose: Tracking people by finding stylized poses. In: Proc. IEEE Conf. on CVPR, pp. 271–278 (2005)
Rosales, R., Athitsos, V., Sclaroff, S.: 3D hand pose reconstruction using specialized mappings. In: Proc. IEEE Conf. on ICCV, pp. 378–385 (2001)
Sigal, L., Bhatia, S., Roth, S., Black, M., Isard, M.: Tracking loose-limbed people. In: Proc. IEEE Conf. on CVPR, pp. 421–428 (2004)
Sminchisescu, C., Kanaujia, A., Li, Z., Metaxas, D.: Discriminative density propagation for 3D human motion estimation. In: Proc. IEEE Conf. on CVPR, pp. 390–397 (2005)
Sminchisescu, C., Triggs, B.: Kinematic jump processes for monocular 3D human tracking. In: Proc. IEEE Conf. on CVPR, pp. 69–76 (2003)
Sminchisescu, C., Triggs, B.: Building roadmaps of minima and transitions in visual models. Int. Journal of Computer Vision 61(1), 81–101 (2005)
Sudderth, E., Ihler, A., Freeman, W., Willsky, A.: Nonparametric belief propagation. In: Proc. IEEE Conf. on CVPR, pp. 605–612 (2003)
Sudderth, E., Mandel, M., Freeman, W., Willsky, A.: Distributed occlusion reasoning for tracking with nonparametric belief propagation. In: NIPS (2004)
Sudderth, E., Mandel, M., Freeman, W., Willsky, A.: Visual hand tracking using nonparametric belief propagation. In: IEEE CVPR Workshop on Generative Model based Vision (2004)
Wang, R., Leow, W.K.: Human body posture refinement by nonparametric belief propagation. In: IEEE Conf. on ICIP (2005)
Yedidia, J.S., Freeman, W.T., Weiss, Y.: Constructing free energy approximations and generalized belief propagation algorithms. Technical report, MERL (2002)
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
Wang, R., Leow, W.K. (2006). Human Posture Analysis Under Partial Self-occlusion. In: Campilho, A., Kamel, M.S. (eds) Image Analysis and Recognition. ICIAR 2006. Lecture Notes in Computer Science, vol 4141. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11867586_79
Download citation
DOI: https://doi.org/10.1007/11867586_79
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-44891-4
Online ISBN: 978-3-540-44893-8
eBook Packages: Computer ScienceComputer Science (R0)