Fast 3D Human Body Gesture Recognition with Multiple Principal Planes Approximation
Pages 236 - 241
Abstract
In applying object reconstruction techniques to the problem of 3D shape approximation, we develop two new and powerful improvements to increase the robustness and accuracy of 3D human body gesture recognition. The first, the moment-preserving principal, solves the problem of 3D shape approximation with multiple surfaces by minimizing the shape reconstruction error. The second, we represents a surface with an affine-invariant surface descriptor for representing a 3D shape with the bag-of-words (BoW) model. The approach also aims at generating a time-ordered pose codebook to speed up the key-poses detection and improve precision. Our experiments demonstrate that these contributions make the 3D human body gesture recognition not only tractable but also highly accurate for our example application.
References
[1]
Alexiadis, D. S., Zarpalas, D., and Daras, P. 2013. Real-time, full 3-D reconstruction of moving foreground objects from multiple consumer depth cameras. IEEE Trans. Multimedia. 15, 2 (2013), 339--358, 2013.
[2]
Klank, U. Zia, M., and Beetz, M. 2009. 3D model Selection from an Internet Database for Robotic Vision. In Proceedings of IEEE Intl. Conf. Robotics and Automation. (May 2009).
[3]
Wang, J., Liu, Z., Wu, Y., and Yuan, J. 2012. Mining actionlet ensemble for action recognition with depth cameras," In Proceedings of IEEE Conf. on Computer Vision and Pattern Recognition. (2012).
[4]
Pierobon, M., Marcon, M., Sarti, A., and Tubaro, S. 2007. A human action classifier from 4-D data (3-D+time) based on an invariant body shape descriptor and hidden Markov models. In Proceedings of Intl. Conf. Signal Processing and Multimedia Applications. (2007), 8--31.
[5]
Barnachon, M., Bouakaz, S., Boufama, B., and Guillou, E. 2014. Ongoing human action recognition with motion capture. Pattern Recognition. 47 (1, 2014), 238--247.
[6]
Niebles, J., Wang, H., and Fei-Fei, L. 2008. Unsupervised learning of human action categories using spatial-temporal words. Intl. Journal of Computer Vision. 79 (3, 2008), 299--318.
[7]
Sakoe, H. and Chiba, S. 1978. Dynamic programming algorithm optimization for spoken word recognition. IEEE Trans. Acoustics, Speech, and Signal Processing. 26 (1978), 43--49.
[8]
Boser, B. E., Guyon, I. M. V., and Vapnik, N. 1992. A training algorithm for optimal margin classifiers. In Proceedings of ACM Int'l Workshop on Computational Learning Theory. (1992).
[9]
Schindler, K. and Gool, L. V. 2008. Action snippets: how many frames does human action recognition require? In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR). (2008).
[10]
Bhattacharyya, A. 1943. On a measure of divergence between two statistical populations defined by their probability distributions. Bulletin of the Calcutta Mathematical Society. 35 (1943), 99--109.
[11]
Cheng, S.-C., Kuo, C.-T. and Wu, D.-C. 2010. A novel 3d mesh compression using mesh segmentation with multiple principal plane analysis. Pattern Recognition. 43 (1, 2010), 261--279.
[12]
Zhang, W., Zhao, D., and Wang, X. 2013. Agglomerative clustering via maximum incremental path integral. Pattern Recognition. 46 (11, 2013), 3056--3065.
Index Terms
- Fast 3D Human Body Gesture Recognition with Multiple Principal Planes Approximation
Recommendations
Human-virtual human interaction by upper body gesture understanding
VRST '13: Proceedings of the 19th ACM Symposium on Virtual Reality Software and TechnologyIn this paper, a novel human-virtual human interaction system is proposed. This system supports a real human to communicate with a virtual human using natural body language. Meanwhile, the virtual human is capable of understanding the meaning of human ...
Combining multiple depth-based descriptors for hand gesture recognition
The hand is reliably extracted from the scene by jointly using color and depth data.Features extracted from depth data allow a reliable hand gesture recognition.Multiple features capturing different properties of the gestures are combined together.The ...
Gesture Recognition Method Using Sensing Blocks
Recently, the recognition of posture and gesture has been widely used in fields such as medical treatment and human---computer interaction. Previous research into the recognition of posture and gesture has mainly used human skeletons and an RGB-D ...
Comments
Information & Contributors
Information
Published In
November 2014
298 pages
ISBN:9781450331845
DOI:10.1145/2683405
- Co-chairs:
- John Perrone,
- Michael Mayo,
- Anthony Blake,
- General Chair:
- Michael J. Cree,
- Publications Chair:
- Lee Streeter
Copyright © 2014 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
In-Cooperation
- The University of Waikato
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 19 November 2014
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
IVCNZ '14
IVCNZ '14: The 29th International Conference on Image and Vision Computing New Zealand
November 19 - 21, 2014
Hamilton, New Zealand
Acceptance Rates
IVCNZ '14 Paper Acceptance Rate 55 of 74 submissions, 74%;
Overall Acceptance Rate 55 of 74 submissions, 74%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 49Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 05 Mar 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in