FiPR: A Fine-grained Human Posture Recognition

2019 ◽  
Author(s):  
Jianyang Ding ◽  
Yong Wang ◽  
Yinghua Qi ◽  
Chengcheng Ma ◽  
Yuan Leng
Keyword(s):  
Human Posture ◽  
Fine Grained ◽  
Posture Recognition

On The Use of Advanced Correlation Filters for Human Posture Recognition

2007 ◽  
Vol 7 (20) ◽  
pp. 2947-2956 ◽  
Author(s):  
Nooritawati Md. Tahir ◽  
Aini Hussain ◽  
Salina Abdul Sama ◽  
Hafizah Husain ◽  
Andrew Teoh Beng Jin
Keyword(s):  
Correlation Filters ◽  
Human Posture ◽  
Posture Recognition

scholarly journals Human Posture Recognition in Intelligent Healthcare

2020 ◽  
Vol 1437 ◽  
pp. 012014 ◽  
Author(s):  
Xinyue Yang ◽  
Xiaoyang Ren ◽  
Meng Chen ◽  
Luqi Wang ◽  
Yuhao Ding
Keyword(s):  
Human Posture ◽  
Posture Recognition ◽  
Intelligent Healthcare

Human Posture Recognition Based on DAG-SVMS

2014 ◽  
Vol 1042 ◽  
pp. 117-120 ◽  
Author(s):  
Shuang Mei Wang ◽  
Yi Gao ◽  
Li Luo
Keyword(s):  
Feature Extraction ◽  
Moving Objects ◽  
Feature Vector ◽  
Moving Object ◽  
Recognition Method ◽  
Human Posture ◽  
Feature Parameters ◽  
Posture Recognition

A posture feature extraction and recognition method in monitoring environment is proposed in this paper which can recognize human shapes and analyze human postures. First contours of moving objects are extracted from two frames of a consecutive monitoring video. Then feature parameters are calculated from boundary contours to construct feature vector. In order to classify moving object and human and analyze postures, a DAG-SVMS is constructed by training 100 sample images. Results demonstrate the validity of this method.

Human Posture Recognition for Estimation of Human Body Condition

2019 ◽  
Vol 23 (3) ◽  
pp. 519-527 ◽  
Author(s):  
Wei Quan ◽  
Jinseok Woo ◽  
Yuichiro Toda ◽  
Naoyuki Kubota ◽  
◽  
...  
Keyword(s):  
Human Subjects ◽  
Research Topic ◽  
Human Robot Interaction ◽  
Training Data ◽  
Forward Kinematics ◽  
Robot Interaction ◽  
Swarm Optimization ◽  
Human Posture ◽  
Series Of Experiments ◽  
Posture Recognition

Human posture recognition has been a popular research topic since the development of the referent fields of human-robot interaction, and simulation operation. Most of these methods are based on supervised learning, and a large amount of training information is required to conduct an ideal assessment. In this study, we propose a solution to this by applying a number of unsupervised learning algorithms based on the forward kinematics model of the human skeleton. Next, we optimize the proposed method by integrating particle swarm optimization (PSO) for optimization. The advantage of the proposed method is no pre-training data is that required for human posture generation and recognition. We validate the method by conducting a series of experiments with human subjects.

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