PT Chapter
AU Jorge Charco
   Angel Sappa
   Boris X. Vintimilla
   Henry Velesaca
TI Human Body Pose Estimation in Multi-view Environments
BT ICT Applications for Smart Cities. Intelligent Systems Reference Library
PY 2022
BP 79
EP 99
VL 224
DI 10.1007/978-3-031-06307-7_5
AB This chapter tackles the challenging problem of human pose estimation in multi-view environments to handle scenes with self-occlusions. The proposed approach starts by first estimating the camera pose—extrinsic parameters—in multi-view scenarios; due to few real image datasets, different virtual scenes are generated by using a special simulator, for training and testing the proposed convolutional neural network based approaches. Then, these extrinsic parameters are used to establish the relation between different cameras into the multi-view scheme, which captures the pose of the person from different points of view at the same time. The proposed multi-view scheme allows to robustly estimate human body joints’ position even in situations where they are occluded. This would help to avoid possible false alarms in behavioral analysis systems of smart cities, as well as applications for physical therapy, safe moving assistance for the elderly among other. The chapter concludes by presenting experimental results in real scenes by using state-of-the-art and the proposed multi-view approaches.
ER