Adrian Bulat
Human Pose Estimation via Convolutional Part Heatmap Regression
Bulat, Adrian; Tzimiropoulos, Georgios
Authors
Georgios Tzimiropoulos
Abstract
This paper is on human pose estimation using Convolutional Neural Networks. Our main contribution is a CNN cascaded architecture specifically designed for learning part relationships and spatial context, and robustly inferring pose even for the case of severe part occlusions. To this end, we propose a detection-followed-by-regression CNN cascade. The first part of our cascade outputs part detection heatmaps and the second part performs regression on these heatmaps. The benefits of the proposed architecture are multi-fold: It guides the network where to focus in the image and effectively encodes part constraints and context. More importantly, it can effectively cope with occlusions because part detection heatmaps for occluded parts provide low confidence scores which subsequently guide the regression part of our net-work to rely on contextual information in order to predict the location of these parts. Additionally, we show that the proposed cascade is flexible enough to readily allow the integration of various CNN architectures for both detection and regression, including recent ones based on residual learning. Finally, we illustrate that our cascade achieves top performance on the MPII and LSP data sets. Code can be downloaded from http://www.cs.nott.ac.uk/~psxab5/
Citation
Bulat, A., & Tzimiropoulos, G. (2016). Human Pose Estimation via Convolutional Part Heatmap Regression. In Computer Vision – ECCV 2016 (717-732). https://doi.org/10.1007/978-3-319-46478-7_44
Conference Name | 14th European Conference on Computer Vision (EECV 2016) |
---|---|
Conference Location | Amsterdam, The Netherlands |
Start Date | Oct 11, 2016 |
End Date | Oct 14, 2016 |
Acceptance Date | Jul 11, 2016 |
Online Publication Date | Sep 16, 2016 |
Publication Date | Sep 16, 2016 |
Deposit Date | Sep 9, 2016 |
Publicly Available Date | Mar 29, 2024 |
Publisher | Springer |
Peer Reviewed | Peer Reviewed |
Volume | 9911 |
Pages | 717-732 |
Series Title | Lecture Notes in Computer Science |
Series ISSN | 1611-3349 |
Book Title | Computer Vision – ECCV 2016 |
ISBN | 978-3-319-46477-0 |
DOI | https://doi.org/10.1007/978-3-319-46478-7_44 |
Keywords | Human pose estimation, Part heatmap regression, Convolutional Neural Networks |
Public URL | https://nottingham-repository.worktribe.com/output/824157 |
Publisher URL | https://link.springer.com/chapter/10.1007/978-3-319-46478-7_44 |
Related Public URLs | http://www.eccv2016.org/ |
Additional Information | The final publication is available at http://link.springer.com/book/10.1007%2F978-3-319-46478-7 Workshops/short Courses were held October 8-10 and 15-16, 2016 |
Files
human_pose_eccv16.pdf
(3.9 Mb)
PDF
You might also like
Object landmark discovery through unsupervised adaptation
(2019)
Journal Article
Artificial intelligence-enhanced multi-material form measurement for additive materials
(2018)
Presentation / Conference
Deep word embeddings for visual speech recognition
(2018)
Conference Proceeding
End-to-end audiovisual speech recognition
(2018)
Conference Proceeding
Synergy between face alignment and tracking via Discriminative Global Consensus Optimization
(2017)
Conference Proceeding
Downloadable Citations
About Repository@Nottingham
Administrator e-mail: digital-library-support@nottingham.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search