The DOI matches its Crossref record; 195 of the 195 references it cites were found in a registry or an index.
Monocular human pose estimation: A survey of deep learning-based methods
Open accessAbstract
Abstract Vision-based monocular human pose estimation, as one of the most fundamental and challenging problems in computer vision, aims to obtain posture of the human body from input images or video sequences. The recent developments of deep learning techniques have been brought significant progress and remarkable breakthroughs in the field of human pose estimation. This survey extensively reviews the recent deep learning-based 2D and 3D human pose estimation methods published since 2014. This paper summarizes the challenges, main frameworks, benchmark datasets, evaluation metrics, performance comparison, and discusses some promising future research directions.
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Metadata
- Authors
- Chen, Y., Tian, Y., & He, M.
- Venue
- Computer Vision and Image Understanding
- Publisher
- Elsevier BV
- Published
- 2020-03
- Volume
- 192
- Pages
- 102897
- ISSN
- 1077-3142 (ISSN-L)
- OpenAlex
- W3000322757
- Cited by
- 441
- References
- 195
- FWCI
- 29.11
- Type
- journal article
- Language
- en
- Open access
- bronze
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