Open AccessMulti person pose estimation based on improved openpose model
Author(s) -
Jiayuan Xing,
Jun Zhang,
Chenxing Xue
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/768/7/072071
Subject(s) - computer science , pose , artificial intelligence , context (archaeology) , convergence (economics) , scheme (mathematics) , key (lock) , set (abstract data type) , data mining , point (geometry) , data set , machine learning , estimation , paleontology , mathematical analysis , geometry , mathematics , computer security , management , economics , biology , programming language , economic growth
In order to solve the problem of multi person pose estimation, a human body pose estimation model framework based on openpose from American Carnegie Mellon University is presented, which uses the COCO (Microsoft common objects in context) data set to augment the data, optimize the network structure and retrain, adjust the learning rate properly and the convergence speed in the early stage of the model, finally using the model to carry out research on human key point detection and visualizing analysis in a multi-person complex scene. It is found that the improved openpose model adopts a bottom to up detection strategy to avoid the influence of the number of human bodies on real-time performance, and has higher key point detection efficiency than the top to down method. The study provides a real-time position feedback scheme for human pose estimation in complex scenes.