Name
Abstract | ||
|---|---|---|
We address the problem of recognizing the visual focus of attention (VFOA) of meeting participants based on their head pose. To this end, the head pose observations are modeled using a Gaussian mixture model (GMM) or a hidden Markov model (HMM) whose hidden states correspond to the VFOA. The novelties of this paper are threefold. First, contrary to previous studies on the topic, in our setup, the potential VFOA of a person is not restricted to other participants only. It includes environmental targets as well (a table and a projection screen), which increases the complexity of the task, with more VFOA targets spread in the pan as well as tilt gaze space. Second, we propose a geometric model to set the GMM or HMM parameters by exploiting results from cognitive science on saccadic eye motion, which allows the prediction of the head pose given a gaze target. Third, an unsupervised parameter adaptation step not using any labeled data is proposed, which accounts for the specific gazing behavior of each participant. Using a publicly available corpus of eight meetings featuring four persons, we analyze the above methods by evaluating, through objective performance measures, the recognition of the VFOA from head pose information obtained either using a magnetic sensor device or a vision-based tracking system. The results clearly show that in such complex but realistic situations, the VFOA recognition performance is highly dependent on how well the visual targets are separated for a given meeting participant. In addition, the results show that the use of a geometric model with unsupervised adaptation achieves better results than the use of training data to set the HMM parameters. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/TSMCB.2008.927274 | IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society |
Keywords | Field | DocType |
meeting participant gaze behavior,gaussian mixture model,geometric model,maximum a posteriori adaptation,cognitive science,magnetic sensor device,gaussian processes,head pose prediction,human factors,visual focus of attention,target tracking,particle filter,pose estimation,hidden markovmodel,visual focus-of attention recognition,head pose tracking,hidden markov model,computer vision,meeting analysis,saccadic eye motion,gesture recognition,hidden markov models,vision-based target tracking system,geometry | Computer vision,Gaze,Computer science,Markov chain,Particle filter,Gesture recognition,Tracking system,Speech recognition,Pose,Artificial intelligence,Hidden Markov model,Mixture model | Journal |
Volume | Issue | ISSN |
39 | 1 | 1941-0492 |
Citations | PageRank | References |
64 | 2.41 | 16 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sileye O. Ba | 1 | 381 | 23.08 |
| Jean-Marc Odobez | 2 | 140 | 19.50 |