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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Weinland, D. Boyer, E. Ronfard, R. |
| Copyright Year | 2007 |
| Description | Author affiliation: LJK INRIA, Grenoble (Weinland, D.; Boyer, E.) |
| Abstract | In this paper, we address the problem of learning compact, view-independent, realistic 3D models of human actions recorded with multiple cameras, for the purpose of recognizing those same actions from a single or few cameras, without prior knowledge about the relative orientations between the cameras and the subjects. To this aim, we propose a new framework where we model actions using three dimensional occupancy grids, built from multiple viewpoints, in an exemplar-based HMM. The novelty is, that a 3D reconstruction is not required during the recognition phase, instead learned 3D exemplars are used to produce 2D image information that is compared to the observations. Parameters that describe image projections are added as latent variables in the recognition process. In addition, the temporal Markov dependency applied to view parameters allows them to evolve during recognition as with a smoothly moving camera. The effectiveness of the framework is demonstrated with experiments on real datasets and with challenging recognition scenarios. |
| Starting Page | 1 |
| Ending Page | 7 |
| File Size | 696854 |
| Page Count | 7 |
| File Format | |
| ISBN | 9781424416301 |
| ISSN | 15505499 |
| DOI | 10.1109/ICCV.2007.4408849 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2007-10-14 |
| Publisher Place | Brazil |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Cameras Hidden Markov models Humans Image recognition Image reconstruction Solid modeling Layout Parametric statistics Kinematics Image motion analysis |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition Software |
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