Name
Abstract | ||
|---|---|---|
The core prerequisite of most modern trackers is a motion assumption, defined as predicting the current location in a limited search region centering at the previous prediction. For clarity, the central subregion of a search region is denoted as the tracking anchor (e.g., the location of the previous prediction in the current frame). However, providing accurate predictions in all frames is very challenging in the complex nature scenes. In addition, the target locations in consecutive frames often change violently under the attribute of fast motion. Both facts are likely to lead the previous prediction to an unbelievable tracking anchor, which will make the aforementioned prerequisite invalid and cause tracking drift. To enhance the reliability of tracking anchors, we propose a real-time multianchor visual tracking mechanism, called multianchor tracking (MAT). Instead of directly relying on the tracking anchor inherited from the previous prediction, MAT selects the best anchor from an anchor ensemble, which includes several objectness-based anchor proposals and the anchor inherited from the previous prediction. The objectness-based anchors provide several complementary selective search regions, and an entropy-minimization-based selection method is introduced to find the best anchor. Our approach offers two benefits: 1) selective search regions can increase the chance of tracking success with affordable computational load and 2) anchor selection introduces the best anchor for each frame, which breaks the limitation of solo depending on the previous prediction. The extensive experiments of nine base trackers upgraded by MAT on four challenging datasets demonstrate the effectiveness of MAT. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TCYB.2020.3039341 | IEEE Transactions on Cybernetics |
Keywords | DocType | Volume |
Reproducibility of Results | Journal | 52 |
Issue | ISSN | Citations |
7 | 2168-2267 | 0 |
PageRank | References | Authors |
0.34 | 35 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhiwen Fang | 1 | 5 | 1.10 |
| Zhiguo Cao | 2 | 314 | 44.17 |
| Yang Xiao | 3 | 237 | 26.58 |
| Kaicheng Gong | 4 | 0 | 0.34 |
| Junsong Yuan | 5 | 3703 | 187.68 |