Extraction of parametric human model for posture recognition using genetic algorithm

2002 ◽  
Author(s):  
Changbo Hu ◽  
Qingfeng Yu ◽  
Yi Li ◽  
Songde Ma
Keyword(s):  
Genetic Algorithm ◽  
Human Model ◽  
Posture Recognition

scholarly journals A Multi-Feature Motion Posture Recognition Model Based on Genetic Algorithm

2021 ◽  
Vol 38 (3) ◽  
pp. 599-605
Author(s):  
Yuanguo Liu ◽  
Ying Wu
Keyword(s):  
Genetic Algorithm ◽  
Feature Fusion ◽  
Fitness Function ◽  
Feature Model ◽  
Support Vector ◽  
Visual Background ◽  
Feature Information ◽  
Posture Recognition ◽  
The Mean ◽  
Mean Variance

The effect of motion posture recognition hinges on the accurate description of motion postures with effective feature information. This study introduces Wronskian function to improve the denoising ability of visual background extractor (ViBe) algorithm, and thus acquires relatively clear motion targets. Then, a multi-feature fusion motion posture feature model was developed based on genetic algorithm (GA). Specifically, GA was called to optimize and fuse the extracted feature information, while a fitness function was constructed based on the mean variance ratio, and used to select the feature information with high inter-class discriminability. Taking support vector machine (SVM) as the classifier, a multi-class classifier was designed by one-to-one method for the classification and recognition of motion postures. Through experiments, our model was proved highly accurate in motion posture recognition.

scholarly journals Applying 3D human model in a posture recognition system

2006 ◽  
Vol 27 (15) ◽  
pp. 1788-1796 ◽  
Author(s):  
Bernard Boulay ◽  
Francois Brémond ◽  
Monique Thonnat
Keyword(s):  
Recognition System ◽  
Human Model ◽  
Posture Recognition

Gait and Posture Analysis Method Based on Genetic Algorithm and Support Vector Machines with Acceleration Data

2016 ◽  
Vol 28 (3) ◽  
pp. 418-424 ◽  
Author(s):  
Huan Gou ◽  
◽  
Tengda Shi ◽  
Lei Yan ◽  
Jiang Xiao
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Parameters Optimization ◽  
Support Vector ◽  
Analysis Method ◽  
Posture Analysis ◽  
Acceleration Data ◽  
Vector Machines ◽  
Posture Recognition ◽  
Simulation Results

[abstFig src='/00280003/18.jpg' width=""300"" text='The result of parameters optimization by GA' ] The support vector machine (SVM) we propose for automated gait and posture recognition is based on acceleration. Acceleration data are obtained from four accelerators attached to the human thigh and lower leg. In the experiment, volunteers take part in four gaits and postures, i.e., sitting, standing, walking and ascending stairs. Acceleration data that are preprocessed include normalization, a wavelet filter and dimension reduction. We used the SVM and a neural network to analyze the data processed. Simulation results indicate that SVM parametersCandgselected by a genetic algorithm (GA) are more effective for gait and posture analysis when compared to the parameterCandgselected by a grid search. The overall classification precision of the four gaits and postures exceeds 90.0%, and neural network simulation results indicate that the SVM using the GA is preferable for use in analysis.

Comparative Psychosocial Pathology: A Human Model of Primate Abusive Behavior

1984 ◽  
Vol 29 (10) ◽  
pp. 781-782
Author(s):  
Gene P. Sackett ◽  
David V. Baldwin
Keyword(s):  
Human Model ◽  
Abusive Behavior

The ground state energy of the ±J spin glass from the genetic algorithm

1994 ◽  
Vol 4 (9) ◽  
pp. 1281-1285 ◽  
Author(s):  
P. Sutton ◽  
D. L. Hunter ◽  
N. Jan
Keyword(s):  
Genetic Algorithm ◽  
Spin Glass ◽  
Ground State Energy ◽  
Ground State ◽  
State Energy
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