Name
Abstract | ||
|---|---|---|
Automatically identifying and analyzing head gestures is useful in many situations like smart meeting rooms and intelligent driver assistance. In this paper, we show that head movements can be broken into its elemental forms (i.e. moving and fixation states) and combinations of these elemental forms give rise to various head gestures. Our approach which we term, Optical flow based Head Movement and Gesture Analyzer (OHMeGA), segments head gestures into moving and fixation states using optical flow tracking and intermittent head pose estimation. OHMeGA runs in real-time, is simple to implement and set up, is robust and is accurate. Furthermore, segmenting head gestures into its elemental forms gives access to higher level semantic information such as fixation time and rate of head motion. Experimental analysis shows promising results. |
Year | Venue | Keywords |
|---|---|---|
2012 | Pattern Recognition | gesture recognition,image segmentation,image sequences,object tracking,OHMeGA,elemental forms,fixation states,head gesture segmentation,higher level semantic information,intermittent head pose estimation,moving states,optical flow tracking,optical flow-based head movement and gesture analyzer |
Field | DocType | ISSN |
Computer vision,Pattern recognition,Computer science,Gesture,Head movements,Gesture recognition,Image segmentation,Pose,Video tracking,Artificial intelligence,Spectrum analyzer,Optical flow | Conference | 1051-4651 |
ISBN | Citations | PageRank |
978-1-4673-2216-4 | 4 | 0.45 |
References | Authors | |
12 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sujitha Martin | 1 | 28 | 1.82 |
| Cuong Tran | 2 | 4 | 0.45 |
| Mohan M. Trivedi | 3 | 6564 | 475.50 |