|Topic:||Získání kinematiky pohybu z videí dětí|
|Supervisor:||Mgr. Matěj Hoffmann, Ph.D.|
|Announce as:||Diplomová práce, Bakalářská práce, Semestrální projekt|
|Description:||To understand the sensorimotor development of children in the first two years after birth, it is important to have quantitative data about their movement kinematics - which joints they use, what are the velocity and acceleration profiles, where in 3D space can they reach etc.
With the development of new computer vision algorithms based on deep learning, it is now possible to extract 3D kinematics from RGB videos of moving people only. However, these algorithms are typically not trained on children. Thanks to collaboration with psychologists, we have collected a number of videos of moving children that we aim to analyze.
|Bibliography:|| Z. Cao, G. H. Martinez, T. Simon, S. Wei, and Y. A. Sheikh, “Openpose: Realtime multi-person 2d pose estimation using part affinity fields,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019. https://github.com/CMU-Perceptual-Computing-Lab/openpose
 Xiu, Y., Li, J., Wang, H., Fang, Y., & Lu, C. (2018). Pose Flow: Efficient online pose tracking. https://arxiv.org/pdf/1802.00977.pdf https://github.com/YuliangXiu/PoseFlow
 G. Pavlakos, V. Choutas, N. Ghorbani, T. Bolkart, A. A. A. Osman, D. Tzionas, and M. J. Black, “Expressive body capture: 3d hands, face, and body from a single image,” in Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2019. https://github.com/vchoutas/smplify-x
 N. Hesse, S. Pujades, J. Romero, M. J. Black, C. Bodensteiner, M. Arens, U. G. Hofmann, U. Tacke, M. Hadders-Algra, R. Weinberger, W. Müller-Felber, and A. Sebastian Schroeder, “Learning an infant body model from rgb-d data for accurate full body motion analysis,” in Medical Image Computing and Computer Assisted Intervention – MICCAI 2018.
Springer International Publishing, 2018, pp. 792–800.