Name
Abstract | ||
|---|---|---|
We introduce a robust multi-object tracking for abstract multi-dimensional feature vectors. The Condensation and the Wavelet Approximated Reduced Vector Machine (W-RVM) approach are joined to spend only as much as necessary effort for easy to discriminate regions (Condensation) and measurement locations (W-RVM) of the feature space, but most for regions and locations with high statistical likelihood to contain the object of interest. The new 3D Cascaded Condensation Tracking (CCT) yields more than 10 times faster tracking than state-of-art detection methods. We demonstrate HCI applications by high resolution face tracking within a large camera scene with an active dual camera system. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1016/j.csi.2011.02.001 | Computer Standards & Interfaces |
Keywords | Field | DocType |
cascaded image,active dual camera system,robust multi-object tracking,efficient object tracking,high statistical likelihood,cascaded condensation tracking,vector machine,high resolution face tracking,hci application,feature space,large camera scene,abstract multi-dimensional feature vector | Computer vision,Feature vector,Computer science,Support vector machine,Image sensing,Video tracking,Artificial intelligence,Facial motion capture,Wavelet | Journal |
Volume | Issue | ISSN |
34 | 6 | 0920-5489 |
Citations | PageRank | References |
2 | 0.39 | 10 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Matthias RäTsch | 1 | 89 | 9.48 |
| Clemens Blumer | 2 | 26 | 3.19 |
| Thomas Vetter | 3 | 4528 | 529.79 |
| Gerd Teschke | 4 | 79 | 11.11 |