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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Al-Hames, M. Lenz, C. Reiter, S. Schenk, J. Wallhoff, F. Rigoll, G. |
| Copyright Year | 2007 |
| Description | Author affiliation: Tech. Univ. Munchen, Munchen (Al-Hames, M.; Lenz, C.; Reiter, S.; Schenk, J.; Wallhoff, F.; Rigoll, G.) |
| Abstract | The asynchronous hidden Markov model (AHMM) models the joint likelihood of two observation sequences, even if the streams are not synchronised. We explain this concept and how the model is trained by the EM algorithm. We then show how the AHMM can be applied to the analysis of group action events in meetings from both clear and disturbed data. The AHMM outperforms an early fusion HMM by 5.7% recognition rate (a rel. error reduction of 38.5%) for clear data. For occluded data, the improvement is in average 6.5% recognition rate (rel. error red. 40%). Thus asynchronity is a dominant factor in meeting analysis, even if the data is disturbed. The AHMM exploits this and is therefore much more robust against disturbances. |
| File Size | 408683 |
| File Format | |
| ISBN | 9781424414369 |
| ISSN | 15224880 |
| DOI | 10.1109/ICIP.2007.4379130 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2007-09-16 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Robustness Videos Hidden Markov models Cameras Organizational aspects Legged locomotion Speech analysis Microphone arrays Man machine systems Signal processing algorithms Multimedia communication Meetings Video signal processing |
| Content Type | Text |
| Resource Type | Article |
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