The DOI matches its Crossref record; 52 of the 52 references it cites were found in a registry or an index.
Decomposing biological motion: A framework for analysis and synthesis of human gait patterns
Open accessAbstract
Biological motion contains information about the identity of an agent as well as about his or her actions, intentions, and emotions. The human visual system is highly sensitive to biological motion and capable of extracting socially relevant information from it. Here we investigate the question of how such information is encoded in biological motion patterns and how such information can be retrieved. A framework is developed that transforms biological motion into a representation allowing for analysis using linear methods from statistics and pattern recognition. Using gender classification as an example, simple classifiers are constructed and compared to psychophysical data from human observers. The analysis reveals that the dynamic part of the motion contains more information about gender than motion-mediated structural cues. The proposed framework can be used not only for analysis of biological motion but also to synthesize new motion patterns. A simple motion modeler is presented that can be used to visualize and exaggerate the differences in male and female walking patterns.
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Cites
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Cited by
AMASS: Archive of Motion Capture As Surface Shapes
Mahmood, N., Ghorbani, N., Troje, N. F., Pons-Moll, G., & Black, M. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00554 · Cited by 1,323
Self-citationNeural mechanisms for the recognition of biological movements
Giese, M. A., & Poggio, T. (2003) · Nature Reviews Neuroscience · doi:10.1038/nrn1057 · Cited by 955
Tardigrades survive exposure to space in low Earth orbit
Jönsson, K. I., Rabbow, E., Schill, R. O., Harms-Ringdahl, M., & Rettberg, P. (2008) · Current Biology · doi:10.1016/j.cub.2008.06.048 · Cited by 415
MotionBERT: A Unified Perspective on Learning Human Motion Representations
Zhu, W., Ma, X., Liu, Z., Liu, L., Wu, W., & Wang, Y. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01385 · Cited by 348
A Study on Gait-Based Gender Classification
Yu, S., Tan, T., Huang, K., Jia, K., & Wu, X. (2009) · IEEE Transactions on Image Processing · doi:10.1109/tip.2009.2020535 · Cited by 293
Metadata
- Authors
- Troje, N. F.
- Venue
- Journal of Vision
- Published
- 2002-09-10
- Volume
- 2
- Issue
- 5
- Pages
- 2
- ISSN
- 1534-7362 (ISSN-L)
- OpenAlex
- W2144481990
- Cited by
- 1,016
- References
- 52
- FWCI
- 28.11
- Type
- journal article
- Language
- en
- Open access
- gold
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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.
