The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Menyasarkan kerumitan pengecaman postur dengan Kinect, kaedah pengecaman postur menggunakan ciri jarak dicadangkan. Pertama, data imej kedalaman dikumpulkan oleh Kinect, dan maklumat koordinat tiga dimensi 20 sendi rangka diperolehi. Kedua, mengikut sumbangan sendi kepada ekspresi postur, data sendi rangka Kinect 60 dimensi telah diubah menjadi vektor ciri jarak 24 dimensi yang dinormalisasi mengikut struktur badan manusia. Ketiga, kaedah pengecaman postur statik bagi jarak terpendek dan kaedah pengecaman postur dinamik bagi jarak terkumpul minimum dengan ledingan masa dinamik (DTW) telah dicadangkan. Keputusan eksperimen menunjukkan bahawa kadar pengecaman postur statik, postur dinamik bukan subjek dan postur dinamik merentas subjek masing-masing adalah 95.9%, 93.6% dan 89.8%. Akhir sekali, pemilihan postur, penempatan Kinect, dan perbandingan dengan literatur telah dibincangkan, yang menyediakan rujukan untuk teknologi pengecaman postur berasaskan Kinect dan reka bentuk interaksi.
Yan LI
Shenyang Sport University
Zhijie CHU
Shenyang University of Technology
Yizhong XIN
Shenyang University of Technology
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Salinan
Yan LI, Zhijie CHU, Yizhong XIN, "Posture Recognition Technology Based on Kinect" in IEICE TRANSACTIONS on Information,
vol. E103-D, no. 3, pp. 621-630, March 2020, doi: 10.1587/transinf.2019EDP7221.
Abstract: Aiming at the complexity of posture recognition with Kinect, a method of posture recognition using distance characteristics is proposed. Firstly, depth image data was collected by Kinect, and three-dimensional coordinate information of 20 skeleton joints was obtained. Secondly, according to the contribution of joints to posture expression, 60 dimensional Kinect skeleton joint data was transformed into a vector of 24-dimensional distance characteristics which were normalized according to the human body structure. Thirdly, a static posture recognition method of the shortest distance and a dynamic posture recognition method of the minimum accumulative distance with dynamic time warping (DTW) were proposed. The experimental results showed that the recognition rates of static postures, non-cross-subject dynamic postures and cross-subject dynamic postures were 95.9%, 93.6% and 89.8% respectively. Finally, posture selection, Kinect placement, and comparisons with literatures were discussed, which provides a reference for Kinect based posture recognition technology and interaction design.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2019EDP7221/_p
Salinan
@ARTICLE{e103-d_3_621,
author={Yan LI, Zhijie CHU, Yizhong XIN, },
journal={IEICE TRANSACTIONS on Information},
title={Posture Recognition Technology Based on Kinect},
year={2020},
volume={E103-D},
number={3},
pages={621-630},
abstract={Aiming at the complexity of posture recognition with Kinect, a method of posture recognition using distance characteristics is proposed. Firstly, depth image data was collected by Kinect, and three-dimensional coordinate information of 20 skeleton joints was obtained. Secondly, according to the contribution of joints to posture expression, 60 dimensional Kinect skeleton joint data was transformed into a vector of 24-dimensional distance characteristics which were normalized according to the human body structure. Thirdly, a static posture recognition method of the shortest distance and a dynamic posture recognition method of the minimum accumulative distance with dynamic time warping (DTW) were proposed. The experimental results showed that the recognition rates of static postures, non-cross-subject dynamic postures and cross-subject dynamic postures were 95.9%, 93.6% and 89.8% respectively. Finally, posture selection, Kinect placement, and comparisons with literatures were discussed, which provides a reference for Kinect based posture recognition technology and interaction design.},
keywords={},
doi={10.1587/transinf.2019EDP7221},
ISSN={1745-1361},
month={March},}
Salinan
TY - JOUR
TI - Posture Recognition Technology Based on Kinect
T2 - IEICE TRANSACTIONS on Information
SP - 621
EP - 630
AU - Yan LI
AU - Zhijie CHU
AU - Yizhong XIN
PY - 2020
DO - 10.1587/transinf.2019EDP7221
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E103-D
IS - 3
JA - IEICE TRANSACTIONS on Information
Y1 - March 2020
AB - Aiming at the complexity of posture recognition with Kinect, a method of posture recognition using distance characteristics is proposed. Firstly, depth image data was collected by Kinect, and three-dimensional coordinate information of 20 skeleton joints was obtained. Secondly, according to the contribution of joints to posture expression, 60 dimensional Kinect skeleton joint data was transformed into a vector of 24-dimensional distance characteristics which were normalized according to the human body structure. Thirdly, a static posture recognition method of the shortest distance and a dynamic posture recognition method of the minimum accumulative distance with dynamic time warping (DTW) were proposed. The experimental results showed that the recognition rates of static postures, non-cross-subject dynamic postures and cross-subject dynamic postures were 95.9%, 93.6% and 89.8% respectively. Finally, posture selection, Kinect placement, and comparisons with literatures were discussed, which provides a reference for Kinect based posture recognition technology and interaction design.
ER -