Hierarchical pose classification based on human physiology for behaviour analysis
- Authors
- Maik, V.; Paik, D. T.; Lim, J.; Park, K.; Paik, J.
- Issue Date
- Mar-2010
- Publisher
- INST ENGINEERING TECHNOLOGY-IET
- Citation
- IET COMPUTER VISION, v.4, no.1, pp 12 - 24
- Pages
- 13
- Journal Title
- IET COMPUTER VISION
- Volume
- 4
- Number
- 1
- Start Page
- 12
- End Page
- 24
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/22594
- DOI
- 10.1049/iet-cvi.2008.0086
- ISSN
- 1751-9632
1751-9640
- Abstract
- This study presents a new approach to classify human body poses by using angular constraints and variations of body joints. Although different classifications of the poses have been previously made, the proposed approach attempts to create a more comprehensive, accurate and extensible classification by integrating all possible poses based on angles of movement in human joints. The angular variations in all body joints can determine any possible poses. The joint angles from the body axis are computed in the three-dimensional space. In order to train and classify the pose in an automated manner, support vector machines (SVMs) were used. Experiments were carried out on both benchmark (CMU dataset) and in-house simulated (POSER dataset) poses to evaluate the performance of the proposed classification scheme.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - Graduate School of Advanced Imaging Sciences, Multimedia and Film > Department of Imaging Science and Arts > 1. Journal Articles
- College of Software > School of Computer Science and Engineering > 1. Journal Articles
![qrcode](https://api.qrserver.com/v1/create-qr-code/?size=55x55&data=https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/22594)
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.