Combining Human Body Shape and Pose Estimation for Robust Upper Body Tracking Using a Depth Sensor

2016 ◽  
pp. 285-301 ◽  
Author(s):  
Thomas Probst ◽  
Andrea Fossati ◽  
Luc Van Gool
Keyword(s):  
Pose Estimation ◽  
Human Body ◽  
Body Shape ◽  
Upper Body ◽  
Depth Sensor ◽  
Body Tracking

Body shape classification and block optimization based on space vector length

2019 ◽  
Vol 31 (1) ◽  
pp. 115-129
Author(s):  
Jie Sun ◽  
Qianyun Cai ◽  
Tao Li ◽  
Lei Du ◽  
Fengyuan Zou
Keyword(s):  
Cross Section ◽  
Human Body ◽  
Body Shape ◽  
The Body ◽  
Upper Body ◽  
Shape Classification ◽  
Content Type ◽  
Vector Length ◽  
Space Vectors ◽  
Three Body

PurposeConsidering two-dimensional features in the body shape classification system cannot fully reflect the three-dimensional (3D) morphological characteristics of human body. The purpose of this paper is to propose a 3D feature based method to characterize and classify the upper body shape of women, and then obtained the corresponding garment block and improved the fitness of clothing.Design/methodology/approachIn this study, the [TC]23D scanner was used to obtain human data, and 15 layers of cross-sections of young females’ upper body were extracted. In total, 240 space vectors were obtained with the center of the bust cross-section as the original point. By using the principal component analysis and K-means clustering analysis, the body shape classification based on the space vectors length was realized. The garment block corresponding to three body types was obtained using the 3D scanning data and the cross-section convex hull, and compared with existing garment block and evaluated fitness of the blocks.FindingsIn total, 11 main components used to characterize the 3D morphological features of young women were obtained, which could explain 95.28 percent features of young women’s upper body. By cluster analysis, the body shape of women was divided into three categories. The block of three body types was obtained by the construction of the convex hull model.Originality/valueThis paper investigates a classification method of the body shape based on space vector length, which can effectively reflect the difference of surface shape of human body and further improve the matching degree of human body and clothing.

scholarly journals Structural feature factors extraction of upper body shape for young female

2021 ◽  
Vol 237 ◽  
pp. 01024
Author(s):  
Yujie Ren ◽  
Hongshu Jin
Keyword(s):  
Structural Feature ◽  
Body Surface ◽  
Human Body ◽  
Body Shape ◽  
Surface Feature ◽  
Upper Body ◽  
Feature Points ◽  
Young Females ◽  
Principle Component ◽  
Body Shapes

Human body shape feature points are the key information and basic unit for human body model we are constructing, which performance the difference of body shapes. The purpose of the study is to extract the structural feature factors related to the upper body surface feature points for young females. The 12 feature points of upper body surface were manually confirmed from anthropometric expertise. A total of 31 measurements items, including 3 body surface measurement and 28 photo measurement, were collected for 33 females college students According to the results of correlation analysis, the feature variables of the width, thickness and height dimension based on 12 feature points significantly respectively correlated to the variables of their coordinate orientation, furthermore, the correlated relationship which reflected the width and height features of neck and shoulders shape mainly affected by local skeletal structures. Then, four principle component factors account for upper body shapes of young females, such as width, thickness, height and shoulder shape with characteristic value all over 1, were extracted by the principal component analysis, and the cumulative contribution rate reached 87.387%. Therefore, a total of 8 feature variables sifted from each principle component factor with a loading coefficient over 0.7 as fundamental typical indicators represent the three-dimensional characteristics of body surface feature points reflecting the divergence of body shapes, and it is useful structural information for individual human body modelling.

scholarly journals Human Body Tracking and Pose Estimation Using Modified Camshift Algorithm

2013 ◽  
Vol 06 (05) ◽  
pp. 37-42 ◽  
Author(s):  
Seung-Jun Hwang ◽  
Jae-Hong Min ◽  
In-Gyu Kim ◽  
Seung-Jae Park ◽  
Gwang-Pyo Ahn ◽  
...  
Keyword(s):  
Pose Estimation ◽  
Human Body ◽  
Camshift Algorithm ◽  
Body Tracking ◽  
Human Body Tracking

scholarly journals A New Multi-Person Pose Estimation Method Using the Partitioned CenterPose Network

2021 ◽  
Vol 11 (9) ◽  
pp. 4241
Author(s):  
Jiahua Wu ◽  
Hyo Jong Lee
Keyword(s):  
Pose Estimation ◽  
Human Body ◽  
State Of The Art ◽  
Estimation Method ◽  
Bottom Up ◽  
Center Point ◽  
Novel Approach ◽  
Body Joints

In bottom-up multi-person pose estimation, grouping joint candidates into the appropriately structured corresponding instance of a person is challenging. In this paper, a new bottom-up method, the Partitioned CenterPose (PCP) Network, is proposed to better cluster the detected joints. To achieve this goal, we propose a novel approach called Partition Pose Representation (PPR) which integrates the instance of a person and its body joints based on joint offset. PPR leverages information about the center of the human body and the offsets between that center point and the positions of the body’s joints to encode human poses accurately. To enhance the relationships between body joints, we divide the human body into five parts, and then, we generate a sub-PPR for each part. Based on this PPR, the PCP Network can detect people and their body joints simultaneously, then group all body joints according to joint offset. Moreover, an improved l1 loss is designed to more accurately measure joint offset. Using the COCO keypoints and CrowdPose datasets for testing, it was found that the performance of the proposed method is on par with that of existing state-of-the-art bottom-up methods in terms of accuracy and speed.

Video-Based Human Motion Analysis

2011 ◽  
Vol 403-408 ◽  
pp. 2593-2597
Author(s):  
Hong Bao ◽  
Zhi Min Liu
Keyword(s):  
Motion Analysis ◽  
Human Body ◽  
Body Shape ◽  
Human Motion ◽  
Random Motion ◽  
Human Motion Analysis ◽  
Mass Center ◽  
Shape Features ◽  
Skeleton Model ◽  
Regular Motion

In the analysis of human motion, movement was divided into regular motion (such as walking and running) and random motion (such as falling down).Human skeleton model is used in this paper to do the video-based analysis. Key joints on human body were chosen to be traced instead of tracking the entire human body. Shape features like mass center trajectory were used to describe the movement, and to classify human motion. desired results achieved.

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