Abstract
The 3D human body tracking from videos in unconstrained scenes is a challenging problem and has widespread applications. In this paper, we introduce a novel framework that incorporates the graph-based human limbs detection into the articulated Bayesian tracking. The 3D human body model with a hierarchical tree structure can describe human’s movement by setting relevant parameters. Particle filter, which is the optimal Bayesian estimation, is used to predict the state of the 3D human pose. In order to compute the likelihood of particles, the pictorial structure model is introduced to detect the human body limbs from monocular uncalibrated images. Then the detected articulated body limbs are matched with each particle using shape contexts. Thus the 3D pose is recovered using a weighted sum of matching costs of all particles. Experimental results show our algorithm can accurately track the walking poses on very long video sequences.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Deutscher, J., Reid, I.: Articulated body motion capture by stochastic search. Int. J. Computer Vision 61(2), 185–205 (2005)
Balan, A., Sigal, L., Black, M.: A quantitative evaluation of video-based 3D person tracking. In: IEEE Workshop on VS-PETS, pp. 349–356 (2005)
Felzenszwalb, P., Huttenlocher, D.: Pictorial structures for object recognition. Int. J. Computer Vision 61(1), 55–79 (2005)
Belongie, S., Malik, J., Puzicha, J.: Shape Matching and Object Recognition Using Shape Contexts. IEEE Trans. Pattern Analysis and Machine Intelligence 24(4), 509–522 (2002)
Arulampalam, M.S., Maskell, S., Gordon, N., Clapp, T.: A tutorial on particle filters for online nonlinear/non-gaussian bayesian tracking. IEEE Transactions on Signal Processing 50(2), 174–188 (2002)
Agarwal, A., Triggs, B.: Recovering 3D Human Pose from Monocular Images. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(1), 44–58 (2006)
Huazhong, N., Xu, W., Gong, Y., Thomas, S.H.: Discriminative Learning of Visual Words for 3D Human Pose Estimation. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2008)
Ronald, P.: Evaluating Example-based Pose Estimation: Experiments on the HumanEva Sets. In: Online proceedings of the Computer Vision and Pattern Recognition workshop on Evaluation of Articulated Human Motion and Pose Estimation (EHuM2), Minneapolis, MN (2007)
Ramanan, D., Forsythm, D.A., Zisserman, A.: Tracking People by Learning their Appearance. IEEE Pattern Analysis and Machine Intelligence (2007)
Bo, L., Sminchisescu, C., Kanaujia, A., Metaxas, D.: Fast Algorithms for Large Scale Conditional 3D Prediction. In: IEEE International Conference on Computer Vision and Pattern Recognition (2008)
Thayananthan, A., Stenger, B., Torr, P.H.S., Cipolla, R.: Shape Context and Chamfer Matching in Cluttered Scenes. In: IEEE International Conference on Computer Vision and Pattern Recognition (2003)
Brubaker, M.A., Fleet, D.J.: The Kneed Walker for Human Pose Tracking. In: IEEE International Conference on Computer Vision and Pattern Recognition (2008)
Vondrak, M., Sigal, L., Jenkins, O.: Physical simulation for probabilistic motion tracking. In: IEEE International Conference on Computer Vision and Pattern Recognition (2008)
Felzenszwalb, P., Huttenlocher, D.: Distance Transforms of Sampled Functions. Cornell Computing and Information Science Technical Report TR2004-1963 (2004)
Papadimitriou, C., Steiglitz, K.: Combinatorial Optimization: Algorithms and Complexity. Prentice Hall, Englewood Cliffs (1982)
Isard, M., Blake, A.: Condensation- Conditional density propagation for visual tracking. Int. J. Computer Vision 29(1), 5–28 (1998)
Matlab Motion Capture Toolbox, http://www.cs.man.ac.uk/~neill/mocap/
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zeng, C., Ma, H., Ming, A., Zhang, X. (2009). 3D Human Body Tracking in Unconstrained Scenes. In: Muneesawang, P., Wu, F., Kumazawa, I., Roeksabutr, A., Liao, M., Tang, X. (eds) Advances in Multimedia Information Processing - PCM 2009. PCM 2009. Lecture Notes in Computer Science, vol 5879. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10467-1_10
Download citation
DOI: https://doi.org/10.1007/978-3-642-10467-1_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-10466-4
Online ISBN: 978-3-642-10467-1
eBook Packages: Computer ScienceComputer Science (R0)