Human Pose Estimation Algorithm Using Optimized Symmetric Spatial Transformation Network

Main Article Content

Shengqing Lin
https://orcid.org/0009-0002-9088-1384
Nor Azizah Ali
https://orcid.org/0000-0003-2565-3836
Azlan bin Mohd Zain
https://orcid.org/0000-0003-2004-3289
Muhalim Mohamed Amin Amin

Abstract

Human posture estimation is a crucial topic in the computer vision field and has become a hotspot for research in many human behaviors related work. Human pose estimation can be understood as the human key point recognition and connection problem. The paper presents an optimized symmetric spatial transformation network designed to connect with single-person pose estimation network to propose high-quality human target frames from inaccurate human bounding boxes, and introduces parametric pose non-maximal suppression to eliminate redundant pose estimation, and applies an elimination rule to eliminate similar pose to obtain unique human pose estimation results. The exploratory outcomes demonstrate the way that the proposed technique can precisely recognize the human central issues, really work on the exactness of human posture assessment, and can adjust to the intricate scenes with thick individuals and impediment. Finally, the difficulties and possible future trends are described, and the development of the field is presented.

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Human Pose Estimation Algorithm Using Optimized Symmetric Spatial Transformation Network. Baghdad Sci.J [Internet]. 2024 Feb. 25 [cited 2024 Oct. 9];21(2(SI):0755. Available from: https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/9775
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How to Cite

1.
Human Pose Estimation Algorithm Using Optimized Symmetric Spatial Transformation Network. Baghdad Sci.J [Internet]. 2024 Feb. 25 [cited 2024 Oct. 9];21(2(SI):0755. Available from: https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/9775

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