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Syntactic model-based human body 3D reconstruction and event classification via association based features mining and deep learning

Title
Syntactic model-based human body 3D reconstruction and event classification via association based features mining and deep learning
Author
김기범
Keywords
2D to 3D reconstruction; Convolutional neural network; Gait event classification; Human posture analysis; Landmark detection; Synthetic model; Electronic computers. Computer science; QA75.5-76.95
Issue Date
2021-11
Publisher
PEERJ INC
Citation
PEERJ COMPUTER SCIENCE, v. 7, Page. 1-36
Abstract
The study of human posture analysis and gait event detection from various types of inputs is a key contribution to the human life log. With the help of this research and technologies humans can save costs in terms of time and utility resources. In this paper we present a robust approach to human posture analysis and gait event detection from complex video-based data. For this, initially posture information, landmark information are extracted, and human 2D skeleton mesh are extracted, using this information set we reconstruct the human 2D to 3D model. Contextual features, namely, degrees of freedom over detected body parts, joint angle information, periodic and non-periodic motion, and human motion direction flow, are extracted. For features mining, we applied the rule-based features mining technique and, for gait event detection and classification, the deep learning-based CNN technique is applied over the mpii-video pose, the COCO, and the pose track datasets. For the mpii-video pose dataset, we achieved a human landmark detection mean accuracy of 87.09% and a gait event recognition mean accuracy of 90.90%. For the COCO dataset, we achieved a human landmark detection mean accuracy of 87.36% and a gait event recognition mean accuracy of 89.09%. For the pose track dataset, we achieved a human landmark detection mean accuracy of 87.72% and a gait event recognition mean accuracy of 88.18%. The proposed system performance shows a significant improvement compared to existing state-of-the-art frameworks.
URI
https://doaj.org/article/27a4025cbc82419297e4590b08f1aa2fhttps://repository.hanyang.ac.kr/handle/20.500.11754/169730
ISSN
2376-5992
DOI
10.7717/peerj-cs.764
Appears in Collections:
ETC[S] > 연구정보
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