The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Looking at people: sensing for ubiquitous and wearable computing
Abstract
The research topic of looking at people, that is, giving machines the ability to detect, track, and identify people and more generally, to interpret human behavior, has become a central topic in machine vision research. Initially thought to be the research problem that would be hardest to solve, it has proven remarkably tractable and has even spawned several thriving commercial enterprises. The principle driving application for this technology is "fourth generation" embedded computing: "smart" environments and portable or wearable devices. The key technical goals are to determine the computer's context with respect to nearby humans (e.g., who, what, when, where, and why) so that the computer can act or respond appropriately without detailed instructions. The paper examines the mathematical tools that have proven successful, provides a taxonomy of the problem domain, and then examines the state of the art. Four areas receive particular attention: person identification, surveillance/monitoring, 3D methods, and smart rooms/perceptual user interfaces. Finally, the paper discusses some of the research challenges and opportunities.
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Cited by
Multimodal human–computer interaction: A survey
Jaimes, A., & Sebe, N. (2007) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2006.10.019 · Cited by 965
Compound facial expressions of emotion
Du, S., Tao, Y., & Martinez, A. M. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1322355111 · Cited by 806
Toward an affect-sensitive multimodal human-computer interaction
Pantic, M., & Rothkrantz, L. J. M. (2003) · Proceedings of the IEEE · doi:10.1109/jproc.2003.817122 · Cited by 791
Learning to recognize objects in egocentric activities
Fathi, A., Ren, X., & Rehg, J. M. (2011) · CVPR 2011 · doi:10.1109/cvpr.2011.5995444 · Cited by 557
Modeling and Prediction of Human Behavior
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Metadata
- Authors
- Pentland, A.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2000
- Volume
- 22
- Issue
- 1
- Pages
- 107-119
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2135192052
- Cited by
- 403
- References
- 6
- FWCI
- 33.31
- Type
- journal article
- Language
- en
- Open access
- closed
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