Open AccessA Novel Multiple Person Pose Estimation Optimization Model Utilizing Genetic Algorithm
Author(s) -
Qing Zhang,
Lei Ding,
Kai-Qing Zhou,
Jian Feng Li
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2129/1/012027
Subject(s) - position (finance) , computer science , pose , genetic algorithm , algorithm , feature (linguistics) , artificial intelligence , human body model , function (biology) , data mining , machine learning , linguistics , philosophy , finance , evolutionary biology , economics , biology
For traditional human pose estimation models rely on a large amount of human body feature information, this paper proposes an optimization model using genetic algorithm to solve the problem of multiple person body part assembly. Different from other human body parts assembly method. The method proposed in this paper depends on the joints position information, namely the sum of the connection distances between the joints as the objective function, and finds the optimal value to obtain the best human pose assembly information. The simulation results show that compared with the traditional OpenPose model, the model proposed in this paper can obtain the same human skeleton using less position information.