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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Ping Liu Jin Wang She, M. Honghai Liu |
| Copyright Year | 2011 |
| Description | Author affiliation: School of Creative Technology, University of Portsmouth, UK (Ping Liu; Honghai Liu) || Institute for Technology Research and Innovation, Deakin University, Geelong, VIC 3217, Australia (Jin Wang; She, M.) |
| Abstract | How to recognize human action from videos captured by modern cameras efficiently and effectively is a challenge in real applications. Traditional methods which need professional analysts are facing a bottleneck because of their shortcomings. To cope with the disadvantage, methods based on computer vision techniques, without or with only a few human interventions, have been proposed to analyse human actions in videos automatically. This paper provides a method combining the three dimensional Scale Invariant Feature Transform (SIFT) detector and the Latent Dirichlet Allocation (LDA) model for human motion analysis. To represent videos effectively and robustly, we extract the 3D SIFT descriptor around each interest point, which is sampled densely from 3D Space-time video volumes. After obtaining the representation of each video frame, the LDA model is adopted to discover the underlying structure-the categorization of human actions in the collection of videos. Public available standard datasets are used to test our method. The concluding part discusses the research challenges and future directions. |
| Starting Page | 12 |
| Ending Page | 17 |
| File Size | 720179 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781424498857 |
| e-ISBN | 9781424498840 |
| DOI | 10.1109/RIISS.2011.5945790 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-04-11 |
| Publisher Place | France |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Latent Dirichlet Allocation Human action recognition 3D SIFT Computational modeling Conferences Humans Detectors Feature extraction Three dimensional displays Videos |
| Content Type | Text |
| Resource Type | Article |
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