The DOI matches its Crossref record.
Model-based tracking of self-occluding articulated objects
Abstract
Computer sensing of hand and limb motion is an important problem for applications in human computer interaction and computer graphics. We describe a framework for local trading of self occluding motion, in which one part of an object obstructs the visibility of another. Our approach uses a kinematic model to predict occlusions and windowed templates to track partially occluded objects. We present offline 3D tracking results for hand motion with significant self occlusion.>
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Citations
Cited by
Human motion tracking for rehabilitation-A survey
Zhou, H., & Hu, H. (2008) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2007.09.001 · Cited by 813
Duric, Z., Gray, W. D., Heishman, R., Li, F., Rosenfeld, A., Schoelles, M. J., Schunn, C., & Wechsler, H. (2002) · Proceedings of the IEEE · doi:10.1109/jproc.2002.801449 · Cited by 185
MoDeep: A Deep Learning Framework Using Motion Features for Human Pose Estimation
Jain, A., Tompson, J., LeCun, Y., & Bregler, C. (2015) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-16808-1_21 · Cited by 142
Metadata
- Authors
- Rehg, J. M., & Kanade, T.
- Venue
- Proceedings of IEEE International Conference on Computer Vision
- Publisher
- IEEE Comput. Soc. Press
- Published
- 2002-11-19
- Pages
- 612-617
- OpenAlex
- W2100905386
- Cited by
- 392
- References
- 0
- FWCI
- 33.18
- Type
- conference paper
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
