The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation
Abstract
We investigate the task of 2D articulated human pose estimation in unconstrained still images. This is extremely challenging because of variation in pose, anatomy, clothing, and imaging conditions. Current methods use simple models of body part appearance and plausible configurations due to limitations of available training data and constraints on computational expense. We show that such models severely limit accuracy. Building on the successful pictorial structure model (PSM) we propose richer models of both appearance and pose, using state-of-the-art discriminative classifiers without introducing unacceptable computational expense. We introduce a new annotated database of challenging consumer images, an order of magnitude larger than currently available datasets, and demonstrate over 50 % relative improvement in pose estimation accuracy over a stateof-the-art method.
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Cites
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Cao, Z., Simon, T., Wei, S.-E., & Sheikh, Y. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.143 · Cited by 7,539
“The spatial model for articulated pose is either based on tree-structured graphical models [10, 30, 4, 3, 26, 40, 17], which parametrically encode the spatial relationship between adjacent parts following a kinematic chain, or non-tree models [37, 31, 20, 18, 8] that augment the tree structure with…”
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Who Left the Dogs Out? 3D Animal Reconstruction with Expectation Maximization in the Loop
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Body Parts Dependent Joint Regressors for Human Pose Estimation in Still Images
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Metadata
- Authors
- Johnson, S., & Everingham, M.
- Venue
- Procedings of the British Machine Vision Conference 2010
- Publisher
- British Machine Vision Association
- Published
- 2010
- Pages
- 12.1-12.11
- OpenAlex
- W2135533529
- Cited by
- 819
- References
- 2
- FWCI
- 11.55
- Type
- conference paper
- Language
- en
- Open access
- closed
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