HUMAN POSTURE RECOGNITION: EIGENSPACE TUNING BY A MEAN EIGENSPACE

2005 ◽  
Vol 05 (04) ◽  
pp. 825-837
Author(s):  
M. MASUDUR RAHMAN ◽  
SEIJI ISHIKAWA
Keyword(s):  
Human Body ◽  
Human Posture ◽  
Human Models ◽  
Posture Recognition ◽  
Body Shapes ◽  
Tuning Scheme

This paper investigates an appearance-change issue due to various human body shapes in an eigenspace analysis, which is responsible for generating person-based eigenspaces employing a conventional eigenspace method. We call this a figure effect in this study for this phenomenon. As a consequence, an appearance-based eigenspace method cannot be effective for recognizing human postures with its present available formulation. We propose to employ a generalized eigenspace for avoiding this problem, which is developed by calculating a mean of some selected eigenspaces. We also investigate a dress effect due to human wearing clothes in this paper. The study proposes image pre-processing by Laplacian of Gaussian (LoG) filter for reducing the dress problem. Since the proposed method tunes a conventional eigenspace as an appropriate method for human posture recognition, the proposed scheme is known as an eigenspace tuning. An eigenspace called a tuned eigenspace is obtained from this tuning scheme and it is used for further recognition of unfamiliar postures. We have tested the proposed approach employing a number of human models wearing various clothes along with their different body shapes, and the significance of the method to human posture recognition has been demonstrated.

scholarly journals Detection of sitting posture using hierarchical image composition and deep learning

2021 ◽  
Vol 7 ◽  
pp. e442
Author(s):  
Audrius Kulikajevas ◽  
Rytis Maskeliunas ◽  
Robertas Damaševičius
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Human Body ◽  
Assisted Living ◽  
Hierarchical Network ◽  
Kinematic Parameters ◽  
Image Composition ◽  
Sitting Posture ◽  
Human Posture ◽  
Posture Recognition

Human posture detection allows the capture of the kinematic parameters of the human body, which is important for many applications, such as assisted living, healthcare, physical exercising and rehabilitation. This task can greatly benefit from recent development in deep learning and computer vision. In this paper, we propose a novel deep recurrent hierarchical network (DRHN) model based on MobileNetV2 that allows for greater flexibility by reducing or eliminating posture detection problems related to a limited visibility human torso in the frame, i.e., the occlusion problem. The DRHN network accepts the RGB-Depth frame sequences and produces a representation of semantically related posture states. We achieved 91.47% accuracy at 10 fps rate for sitting posture recognition.

Addiction to medical aesthetic procedures

2018 ◽  
Vol XIV ◽  
pp. 233-249
Author(s):  
Jolanta Kozaczyńska
Keyword(s):  
Social Functioning ◽  
Human Body ◽  
Surgical Intervention ◽  
Common Phenomenon ◽  
Aesthetic Medicine ◽  
Utopian Vision ◽  
Frequent Contact ◽  
The Media ◽  
Body Shapes ◽  
Aesthetic Procedures

Today’s media disseminate a narcissistic cultivation of beauty and promote a focus mainly on appearance and satisfaction from its improvement. The human body assumes a form in the media that is often impossible to achieve without surgical intervention. When people are in frequent contact with a utopian vision of the perfect body, this can lead to many disorders in both social functioning and self-perception. In extreme cases, striving to preserve beauty and youth may lead to an addiction to aesthetic medicine treatments. It is an increasingly common phenomenon. People who are addicted to treatments improving their beauty or changing their body shapes are not aware of the problem that affects them. They lose their rational judgement and their assessment is far from the opinions of people around them and socially accepted norms. All signs of concern from others are perceived as an attack on their independence and this further deepens their sense of loneliness and isolation from society. With time, undergoing further beautifying procedures becomes the only way they know to achieve a momentary sense of happiness.

scholarly journals BodyNet: Volumetric Inference of 3D Human Body Shapes

2018 ◽  
pp. 20-38 ◽  
Author(s):  
Gül Varol ◽  
Duygu Ceylan ◽  
Bryan Russell ◽  
Jimei Yang ◽  
Ersin Yumer ◽  
...  
Keyword(s):  
Human Body ◽  
Body Shapes

scholarly journals Inside Humans: Creating a Simple Layered Anatomical Model from Human Surface Scans

2021 ◽  
Vol 2 ◽  
Author(s):  
Martin Komaritzan ◽  
Stephan Wenninger ◽  
Mario Botsch
Keyword(s):  
Human Body ◽  
Computer Animation ◽  
Physical Simulation ◽  
The Body ◽  
Surface Layers ◽  
Novel Approach ◽  
Body Shapes ◽  
Surface Scan ◽  
Morphable Models ◽  
Muscle Models

3D morphable models are widely used to describe the variation of human body shapes. However, these models typically focus on the surface of the human body, since the acquisition of the volumetric interior would require prohibitive medical imaging. In this paper we present a novel approach for creating a volumetric body template and for fitting this template to the surface scan of a person in a just a few seconds. The body model is composed of three surface layers for bones, muscles, and skin, which enclose the volumetric muscle and fat tissue in between them. Our approach includes a data-driven method for estimating the amount of muscle mass and fat mass from a surface scan, which provides more accurate fits to the variety of human body shapes compared to previous approaches. We also show how to efficiently embed fine-scale anatomical details, such as high resolution skeleton and muscle models, into the layered fit of a person. Our model can be used for physical simulation, statistical analysis, and anatomical visualization in computer animation and medical applications, which we demonstrate on several examples.

Gender Recognition Using 3-D Human Body Shapes

2011 ◽  
Vol 41 (6) ◽  
pp. 898-908 ◽  
Author(s):  
Jinshan Tang ◽  
Xiaoming Liu ◽  
Huaining Cheng ◽  
Kathleen M. Robinette
Keyword(s):  
Human Body ◽  
Gender Recognition ◽  
Body Shapes
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