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Discovering Multi-relational Latent Attributes by Visual Similarity Networks Read chapter Read first chapter

2015 | OriginalPaper | Chapter

Full-Body Human Pose Estimation from Monocular Video Sequence via Multi-dimensional Boosting Regression

Authors : Yonghui Du, Yan Huang, Jingliang Peng

Published in: Computer Vision - ACCV 2014 Workshops

Publisher: Springer International Publishing

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Abstract

In this work, we propose a scheme to estimate two-dimensional full-body human poses in a monocular video sequence. For each frame in the video, we detect the human region using a support vector machine, and estimate the full-body human pose in the detected region using multi-dimensional boosting regression. For the human pose estimation, we design a joints relationship tree, corresponding to the full hierarchical structure of joints in a human body. Further, we make a complete set of spatial and temporal feature descriptors for each frame. Utilizing the well-designed joints relationship tree and feature descriptors, we learn a hierarchy of regressors in the training stage and employ the learned regressors to determine all the joint’s positions in the testing stage. As experimentally demonstrated, the proposed scheme achieves outstanding estimation performance.

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Metadata
Title
Full-Body Human Pose Estimation from Monocular Video Sequence via Multi-dimensional Boosting Regression
Authors
Yonghui Du
Yan Huang
Jingliang Peng
Copyright Year
2015
Book
Computer Vision - ACCV 2014 Workshops

Print ISBN: 978-3-319-16633-9

Electronic ISBN: 978-3-319-16634-6

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-16634-6_39

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