Name
Abstract | ||
|---|---|---|
We present a method for representing, tracking and human following by fusing distributed multiple vision systems in ISpace, with application to pedestrian tracking in a crowd. And the article presents the integration of color distributions into particle filtering. Particle filters provide a robust tracking framework under ambiguity conditions. We propose to track the moving objects by generating hypotheses not in the image plan but on the top-view reconstruction of the scene. Comparative results on real video sequences show the advantage of our method for multi-object tracking. Simulations are carried out to evaluate the proposed performance. Also, the method is applied to the intelligent environment and its performance is verified by the experiments. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/978-3-540-36668-3_134 | PRICAI |
Keywords | Field | DocType |
motion tracking,particle filter,vision system,object tracking | Intelligent environment,Computer vision,Machine vision,Computer science,Particle filter,Filter (signal processing),Tracking system,Video tracking,Artificial intelligence,Motion estimation,Match moving | Conference |
Volume | ISSN | Citations |
4099 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 4 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| TaeSeok Jin | 1 | 49 | 14.52 |
| Changhoon Park | 2 | 46 | 10.97 |
| Soohong Park | 3 | 72 | 22.26 |