Dual Aligned Siamese Dense Regression Tracker
Anchor or anchor-free based Siamese trackers have achieved the astonishing advancement. However, their parallel regression and classification branches lack the tracked target information link and interaction, and the corresponding independent optimization maybe lead to task-misalignment, such as the...
| Publié dans: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - 31(2022) vom: 01., Seite 3630-3643 |
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| Auteur principal: | |
| Autres auteurs: | , , , , |
| Format: | Article en ligne |
| Langue: | English |
| Publié: |
2022
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| Accès à la collection: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society |
| Sujets: | Journal Article |
| Résumé: | Anchor or anchor-free based Siamese trackers have achieved the astonishing advancement. However, their parallel regression and classification branches lack the tracked target information link and interaction, and the corresponding independent optimization maybe lead to task-misalignment, such as the reliable classification prediction with imprecisely localization and vice versa. To address this problem, we develop a general Siamese dense regression tracker (SDRT) with both task and feature alignments. It consists of two cooperative and mutual-guidance core branches: dense local regression with RepPoint representation, the global and local multi-classifier fusion with aligned features. They complement and boost each other to constrain the results with well-localized followed to also be well-classified. Specifically, a dense local regression with RepPoint representation, directly estimates and averages multiple dense local bounding box offsets for accurate localization. And then, the refined bounding boxes can be used to learn the global and local affine alignment features for reliable multi-classifier fusion. The classified scores in turn guide the assigned positive bounding boxes for the regression task. The mutual guidance operations can bridge the connection between classification and regression substantially, since the assigned labels of one task depend on the prediction quality of the other task. The proposed tracking module is general, and it can boost both the anchor or anchor-free based Siamese trackers to some extent. The extensive tracking comparisons on six tracking benchmarks verify its favorable and competitive performance over states-of-the-arts tracking modules |
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| Description: | Date Revised 27.05.2022 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
| ISSN: | 1941-0042 |
| DOI: | 10.1109/TIP.2022.3166638 |