Efficient Monocular Human Pose Estimation Based on Deep Learning Methods: A Survey

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dc.citation.volume12
dc.contributor.authorYan X
dc.contributor.authorLiu B
dc.contributor.authorQu G
dc.date.accessioned2024-10-01T22:13:08Z
dc.date.available2024-10-01T22:13:08Z
dc.date.issued2024-05-09
dc.description.abstractHuman pose estimation (HPE) is a crucial computer vision task with a wide range of applications in sports medicine, healthcare, virtual reality, and human-computer interaction. The demand for real-time HPE solutions necessitates the development of efficient deep-learning models that can be deployed on resource-constrained devices. While a few surveys exist in this area, none delve deeply into the critical intersection of efficiency and performance. This survey reviews the state-of-the-art efficient deep learning approaches for real-time HPE, focusing on strategies for improving efficiency without compromising accuracy. We discuss popular backbone networks for HPE, model compression techniques, network pruning and quantization, knowledge distillation, and neural architecture search methods. Furthermore, we critically analyze the existing works, highlighting their strengths, weaknesses, and applicability to different scenarios. We also present an overview of the evaluation datasets, metrics, and design for efficient HPE. Finally, we identify research gaps and challenges in the field, providing insights and recommendations for future research directions in developing efficient and scalable HPE solutions.
dc.description.confidentialfalse
dc.format.pagination72650-72661
dc.identifier.citationYan X, Liu B, Qu G. (2024). Efficient Monocular Human Pose Estimation Based on Deep Learning Methods: A Survey. IEEE Access. 12. (pp. 72650-72661).
dc.identifier.doi10.1109/ACCESS.2024.3399222
dc.identifier.eissn2169-3536
dc.identifier.elements-typejournal-article
dc.identifier.urihttps://mro.massey.ac.nz/handle/10179/71552
dc.languageEnglish
dc.publisherIEEE
dc.publisher.urihttps://ieeexplore.ieee.org/document/10528302
dc.relation.isPartOfIEEE Access
dc.rights(c) The author/sen
dc.rights.licenseCC BY-NC-NDen
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.subjectSurvey
dc.subject2D human pose estimation
dc.subject3D human pose estimation
dc.subjectdeep learning
dc.subjectefficiency
dc.titleEfficient Monocular Human Pose Estimation Based on Deep Learning Methods: A Survey
dc.typeJournal article
pubs.elements-id489074
pubs.organisational-groupOther
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