The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Space-time gestures
Abstract
A method for learning, tracking, and recognizing human gestures using a view-based approach to model articulated objects is presented. Objects are represented using sets of view models, rather than single templates. Stereotypical space-time patterns, i.e., gestures, are then matched to stored gesture patterns using dynamic time warping. Real-time performance is achieved by using special purpose correlation hardware and view prediction to prune as much of the search space as possible. Both view models and view predictions are learned from examples. Results showing tracking and recognition of human hand gestures at over 10 Hz are presented.>
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Citations
Cites
Dynamic Programming Algorithm Optimization for Spoken Word Recognition
Sakoe, H., & Chiba, S. (1978) · IEEE Transactions on Acoustics Speech and Signal Processing · doi:10.1109/tassp.1978.1163055 · Cited by 6,727
Cited by
Movement, activity and action: the role of knowledge in the perception of motion
Bobick, A. F. (1997) · Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences · doi:10.1098/rstb.1997.0108 · Cited by 294
(1995) · DSpace@MIT (Massachusetts Institute of Technology)
Metadata
- Authors
- Darrell, T., & Pentland, A.
- Venue
- Proceedings of IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE Comput. Soc. Press
- Published
- 2002-12-30
- Pages
- 335-340
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2130506643
- Cited by
- 353
- References
- 1
- FWCI
- 22.79
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Darrell, T., & Pentland, A. (2002). Space-time gestures. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/cvpr.1993.341109
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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.
