Deep Fully-Connected Part-Based Models for Human Pose Estimation
Abstract
We propose a 2D multi-level appearance representation of the human body in RGB images, spatially modelled using a fully-connected graphical model. The appearance model is based on a CNN body part detector, which uses shared features in a cascade architecture to simultaneously detect body parts with different levels of granularity. We use a fully-connected Conditional Random Field (CRF) as our spatial model, over which approximate inference is efficiently performed using the Mean-Field algorithm, implemented as a Recurrent Neural Network (RNN). The stronger visual support from body parts with different levels of granularity, along with the fully-connected pairwise spatial relations, which have their weights learnt by the model, improve the performance of the bottom-up part detector. We adopt an end-to-end training strategy to leverage the potential of both our appearance and spatial models, and achieve competitive results on the MPII and LSP datasets.
Cite
Text
de Bem et al. "Deep Fully-Connected Part-Based Models for Human Pose Estimation." Proceedings of The 10th Asian Conference on Machine Learning, 2018.Markdown
[de Bem et al. "Deep Fully-Connected Part-Based Models for Human Pose Estimation." Proceedings of The 10th Asian Conference on Machine Learning, 2018.](https://mlanthology.org/acml/2018/debem2018acml-deep/)BibTeX
@inproceedings{debem2018acml-deep,
title = {{Deep Fully-Connected Part-Based Models for Human Pose Estimation}},
author = {de Bem, Rodrigo and Arnab, Anurag and Golodetz, Stuart and Sapienza, Michael and Torr, Philip},
booktitle = {Proceedings of The 10th Asian Conference on Machine Learning},
year = {2018},
pages = {327-342},
volume = {95},
url = {https://mlanthology.org/acml/2018/debem2018acml-deep/}
}