Multi-view human pose estimation using modified five-point skeleton model
Chen, Daniel, Chou, Pi-Chi, Fookes, Clinton B., & Sridharan, Sridha (2008) Multi-view human pose estimation using modified five-point skeleton model. In International Conference on Signal Processing and Communication Systems 2007, 17-19 Dec 2007, Gold Coast, Australia.
This paper examines the task of estimating the 3D pose of a human subject acquired from multiple views within a multiple camera surveillance network. We utilised a modified five-point skeleton model with potential application in human action recognition and gait recognition. This paper proposes automatic initialisation and recovery of human pose. Feature tracking and motion prediction are incorporated to increase the accuracy and the robustness of the model. Although the model is tested within the area of video surveillance, it has the potential to extend to other areas such as Virtual Reality, content based retrieval and compression of video. The proposed algorithm is evaluated with the IXMAS database and is demonstrated to produce promising results for 3D pose estimation from a multi-view camera network. Outcomes are also evaluated using feature trackers.
Citation countsare sourced monthly fromand citation databases.
These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science generally from 1980 onwards.
Citations counts from theindexing service can be viewed at the linked Google Scholar™ search.
Full-text downloadsdisplays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.
|Item Type:||Conference Paper|
|Additional Information:||The contents of this paper can be freely accessed online via the conference's web page (see hypertext link).|
|Subjects:||Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Computer Vision (080104)|
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Image Processing (080106)
|Divisions:||Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering|
Past > Schools > School of Engineering Systems
|Copyright Owner:||Copyright 2008 [please consult the authors]|
|Deposited On:||06 Feb 2009 10:11|
|Last Modified:||29 Feb 2012 23:47|
Repository Staff Only: item control page