Name
Abstract | ||
|---|---|---|
We propose an adaptive eye tracking system for robust human-computer interaction under dynamically changing environments based on the partially observable Markov Decision Process POMDP. In our system, real-time eye tracking optimization is tackled using a flexible world-context model based POMDP approach that requires less data and time in adaptation than those of hard world-context model approaches. The challenge is to divide the huge belief space into world-context models, and to search for optimal control parameters in the current world-context model with real-time constraints. The offline learning determines multiple world-context models based on image-quality analysis over the joint space of transition, observation, reward distributions, and an approximate world-context model is balanced with the online learning over a localized horizon. The online learning is formulated as a dynamic parameter control with incomplete information under real-time constraints, and is solved by the real-time Q-learning approach. Extensive experiments conducted using realistic videos have provided us with very encouraging results. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-20904-3_37 | ICVS |
Keywords | Field | DocType |
Eye tracking, POMDP, Real-time Q-learning, World-context model, Image-quality analysis | Offline learning,Eye tracking system,Online learning,Computer vision,Optimal control,Computer science,Partially observable Markov decision process,Eye tracking,Artificial intelligence,Parameter control,Machine learning,Complete information | Conference |
Volume | ISSN | Citations |
9163 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 17 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ji Hye Rhee | 1 | 0 | 0.68 |
| Won Jun Sung | 2 | 9 | 1.67 |
| Mi Young Nam | 3 | 61 | 15.03 |
| Hyeran Byun | 4 | 505 | 65.97 |
| Phill Kyu Rhee | 5 | 60 | 24.82 |