Abstract
In this paper, we present a robust algorithm to capture rapid human motion with self-occlusion. Instead of predicting the position of each human feature, the interest-region of full body is estimated. Then candidate features are extracted through the overall search in the interest-region. To establish the correspondence between candidate features and actual features, an adaptive Bayes classifier is constructed based on the time-varied models of feature attributions. At last, a hierarchical human feature model is adopted to verify and accomplish the feature correspondence. To improve the efficiency, we propose a multiresolution search strategy: the initial candidate feature set is estimated at the low resolution image and successively refined at higher resolution levels. The experiment demonstrates the effectiveness of our algorithm.
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
Xiaoming Liu, Yueting Zhuang, Yunhe Pan. Video Based Human Animation Technique. ACM Multimedia’99 10/99 Orlando, FL, USA, pages 353–362, 1999.
C. Bregler and J. Malik. Tracking people with twists and exponential maps. In Proceeding of IEEE Conference Computer Vision Pattern Recognition, Santa Barbara, CA, pages 8–15, June 1998
P. Fua, A. Gruen, R. Plankers, N. D’Apuzzo, and D. Thalmann. Human body modeling and motion analysis form video sequences, in International Archives of Photogrammetry and Remote Sensing, vol, 32, pages 866–873, Hakodate, Jap, 1998
S. S. Intille and A. F. Bobick. Closed world tracking. In Proceedings of the Fifth International Conference on Computer Vision, pages 672–678, Boston, MA, June 20–23,1995, IEEE Computer Society Press
J. Rehg and T. Kanade. Model-based tracking of self-occluding articulated objects. In Proceedings of the Fifth International Conference on Computer Vision, pages 612–617, Boston, MA, June 20–23,1995, IEEE Computer Society Press
Moeslund, T. B. and E. Granum: 2001, A survey of computer vision-based human motion capture. Computer Vision and Image Understanding 18, 231–268.
Luo Zhongxiang, Zhuang Yueting and Liu Feng. Incomplete Motion Feature Tracking Algorithm in Video Sequences. ICIP 2002.
F. Sebastian Grassia, Motion Editing: Mathematical Foundations, in course: Motion Editing: Principles, Practice, and Promise, in Proceedings of SIGGRAPH 2000, July 23–28, 2000. New Orleans, Louisiana, USA
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Liu, F., Zhuang, Y., Luo, Z., Pan, Y. (2002). A Robust Algorithm for Video Based Human Motion Tracking. In: Chen, YC., Chang, LW., Hsu, CT. (eds) Advances in Multimedia Information Processing — PCM 2002. PCM 2002. Lecture Notes in Computer Science, vol 2532. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36228-2_144
Download citation
DOI: https://doi.org/10.1007/3-540-36228-2_144
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-00262-8
Online ISBN: 978-3-540-36228-9
eBook Packages: Springer Book Archive