Name
Abstract | ||
|---|---|---|
Despite the success of many advanced tracking methods in this area, tracking targets with drastic variation of appearance such as deformation, view change and partial occlusion in video sequences is still a challenge in practical applications. In this paper, we take these serious tracking problems into account simultaneously, proposing a dynamic graph based model to track object and its deformable parts at multiple resolutions. The method introduces well learned structural object detection models into object tracking applications as prior knowledge to deal with deformation and view change. Meanwhile, it explicitly formulates partial occlusion by integrating spatial potentials and temporal potentials with an unparameterized occlusion handling mechanism in the dynamic conditional random field framework. Empirical results demonstrate that the method outperforms state-of-the-art trackers on different challenging video sequences. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ICIP.2014.7025095 | ICIP |
Keywords | DocType | Volume |
video signal processing,spatial potentials,multiple image resolutions,video sequences,temporal potentials,image resolution,unparameterized occlusion handling mechanism,structural object detection models,deformable part tracking method,deformable part based model,view change,conditional random field,object tracking,image sequences,dynamic conditional random field framework,object detection,computer vision,graph theory,dynamic graph based model,partial occlusion | Journal | abs/1311.0262 |
ISSN | Citations | PageRank |
1522-4880 | 0 | 0.34 |
References | Authors | |
15 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Suofei Zhang | 1 | 34 | 7.26 |
| Xu Cheng | 2 | 43 | 7.36 |
| Xu Cheng | 3 | 43 | 7.36 |
| Lin Zhou | 4 | 0 | 1.69 |
| Zhenyang Wu | 5 | 154 | 17.52 |