TY - JOUR
T1 - Robust pose estimation for human body hidden with a cover using body shape feature obtained by normal vector
AU - Kudo, Yuta
AU - Sashida, Takehiko
AU - Aoki, Yoshimitsu
N1 - Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2014/12/1
Y1 - 2014/12/1
N2 - In this paper, we present a robust pose estimation method of a human body hidden with a cover. Our goal is an automatically monitoring system of sleeping human using noncontact and noninvasive sensors for elderly care. We propose a new method for robust human pose estimation from a single depth image using human body shape ITXXICI constructed by nonnal vector infonnation. This shape model is able to represent shapes of rough body, and is effective in robust pose estimation for a person who placed iiiton and blanket. In our method, first, head position is detected from a depth image using SVM. Then, body region is detected by comparing human body sliape model. Head position is used as initial position of body region detecting. Body region is composed of many small rectangles. Next, body region is divided into three body parts by distance between parts. Finally, each part pose is detennined by a linear estimation using point clouds of right and left of the rectangle in each body region.
AB - In this paper, we present a robust pose estimation method of a human body hidden with a cover. Our goal is an automatically monitoring system of sleeping human using noncontact and noninvasive sensors for elderly care. We propose a new method for robust human pose estimation from a single depth image using human body shape ITXXICI constructed by nonnal vector infonnation. This shape model is able to represent shapes of rough body, and is effective in robust pose estimation for a person who placed iiiton and blanket. In our method, first, head position is detected from a depth image using SVM. Then, body region is detected by comparing human body sliape model. Head position is used as initial position of body region detecting. Body region is composed of many small rectangles. Next, body region is divided into three body parts by distance between parts. Finally, each part pose is detennined by a linear estimation using point clouds of right and left of the rectangle in each body region.
KW - 3D image
KW - Covered body pose estimation
KW - Monitoring system
KW - Oriented nonnal vector
KW - Pose estimation
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U2 - 10.2493/jjspe.80.1166
DO - 10.2493/jjspe.80.1166
M3 - Article
AN - SCOPUS:84931421441
SN - 0912-0289
VL - 80
SP - 1166
EP - 1175
JO - Seimitsu Kogaku Kaishi/Journal of the Japan Society for Precision Engineering
JF - Seimitsu Kogaku Kaishi/Journal of the Japan Society for Precision Engineering
IS - 12
ER -