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A survey of advances in vision-based human motion capture and analysis
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Cites
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A survey on vision-based human action recognition
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Expressive Body Capture: 3D Hands, Face, and Body From a Single Image
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A survey of video datasets for human action and activity recognition
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Wireless Sensing for Human Activity: A Survey
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Learning human–environment interactions using conformal tactile textiles
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A Survey of Sensors in Healthcare Workflow Monitoring
Antunes, R. S., Seewald, L. A., Rodrigues, V. F., Costa, C. A. D., Jr., L. G., Righi, R. R., Maier, A., Eskofier, B., Ollenschläger, M., Naderi, F., et al. (2019) · ACM Computing Surveys · doi:10.1145/3177852 · Cited by 48
A Systematic Review of Recent Deep Learning Approaches for 3D Human Pose Estimation
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Global temporal registration of multiple non-rigid surface sequences
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Self-citationAutomated cyclist data collection under high density conditions
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Metadata
- Authors
- Moeslund, T. B., Hilton, A., & Krüger, V.
- Venue
- Computer Vision and Image Understanding
- Publisher
- Elsevier BV
- Published
- 2006-11
- Volume
- 104
- Issue
- 2-3
- Pages
- 90-126
- ISSN
- 1077-3142 (ISSN-L)
- OpenAlex
- W2119350939
- Cited by
- 2,541
- References
- 8
- FWCI
- 152.51
- Type
- journal article
- 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.
