Name
Abstract | ||
|---|---|---|
Hand pose estimation from video is essential for a number of applications such as automatic sign language recognition and robot learning from demonstration. However, hand pose estimation is made difficult by the high degree of articulation of the hand; a realistic hand model is described with at least 35 dimensions, which means that it can assume a wide variety of poses, and there is a very high degree of self occlusion for most poses. Furthermore, different parts of the hand display very similar visual appearance; it is difficult to tell fingers apart in video. These properties of hands put hard requirements on visual features used for hand pose estimation and tracking. In this paper, we evaluate three different state-of-the-art visual shape descriptors, which are commonly used for hand and human body pose estimation. We study the nature of the mappings from the hand pose space to the feature spaces spanned by the visual descriptors, in terms of the smoothness, discriminability, and generativity of the pose-feature mappings, as well as their robustness to noise in terms of these properties. Based on this, we give recommendations on in which types of applications each visual shape descriptor is suitable. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/FG.2013.6553698 | Automatic Face and Gesture Recognition |
Keywords | Field | DocType |
feature extraction,object tracking,pose estimation,video signal processing,feature spaces,hand articulation degree,hand pose estimation,hand pose inference,hand pose space,hand pose tracking,human body pose estimation,pose-feature mapping discriminability,pose-feature mapping generativity,pose-feature mapping smoothness,realistic hand model,self occlusion,video,visual shape descriptors,visual shape features | Robot learning,Computer vision,Pattern recognition,Computer science,3D pose estimation,Pose,Feature extraction,Robustness (computer science),Video tracking,Artificial intelligence,Articulated body pose estimation,Visual appearance | Conference |
ISSN | ISBN | Citations |
2326-5396 | 978-1-4673-5544-5 | 12 |
PageRank | References | Authors |
0.65 | 21 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Akshaya Thippur | 1 | 12 | 0.65 |
| carl henrik ek | 2 | 327 | 30.76 |
| hedvig kjellstrom | 3 | 491 | 42.24 |