Extraction of parametric human model for posture recognition using genetic algorithm

Author(s):  
Changbo Hu ◽  
Qingfeng Yu ◽  
Yi Li ◽  
Songde Ma
2021 ◽  
Vol 38 (3) ◽  
pp. 599-605
Author(s):  
Yuanguo Liu ◽  
Ying Wu

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.


2006 ◽  
Vol 27 (15) ◽  
pp. 1788-1796 ◽  
Author(s):  
Bernard Boulay ◽  
Francois Brémond ◽  
Monique Thonnat

2016 ◽  
Vol 28 (3) ◽  
pp. 418-424 ◽  
Author(s):  
Huan Gou ◽  
◽  
Tengda Shi ◽  
Lei Yan ◽  
Jiang Xiao

[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.


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

1994 ◽  
Vol 4 (9) ◽  
pp. 1281-1285 ◽  
Author(s):  
P. Sutton ◽  
D. L. Hunter ◽  
N. Jan

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