Name
Abstract | ||
|---|---|---|
We present a new approach to modeling and processing multimedia data. This approach is based on graphical models that combine audio and video variables. We demonstrate it by developing a new algorithm for tracking a moving object in a cluttered, noisy scene using two microphones and a camera. Our model uses unobserved variables to describe the data in terms of the process that generates them. It is therefore able to capture and exploit the statistical structure of the audio and video data separately, as well as their mutual dependencies. Model parameters are learned from data via an EM algorithm, and automatic calibration is performed as part of this procedure. Tracking is done by Bayesian inference of the object location from data. We demonstrate successful performance on multimedia clips captured in real world scenarios using off-the-shelf equipment. |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1007/3-540-47969-4_49 | ECCV |
Keywords | Field | DocType |
probabilistic graphical models,video data,audio-video sensor fusion,video variable,processing multimedia data,object location,model parameter,new algorithm,new approach,em algorithm,multimedia clip,graphical model,sensor fusion,bayesian inference | Audio signal,Computer vision,Bayesian inference,Computer science,Expectation–maximization algorithm,Exploit,Sensor fusion,Video tracking,Artificial intelligence,Graphical model,Machine learning,Calibration | Conference |
Volume | ISSN | ISBN |
2350 | 0302-9743 | 3-540-43745-2 |
Citations | PageRank | References |
19 | 1.51 | 6 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Matthew J. Beal | 1 | 600 | 64.31 |
| Hagai Attias | 2 | 856 | 96.36 |
| Nebojsa Jojic | 3 | 1397 | 165.68 |