Name
Abstract | ||
|---|---|---|
Recently, in the field of visual object tracking, visual object tracking algorithms combined with visual object segmentation have achieved impressive results while using mask to label targets in the VOT2020 dataset. Most of the trackers get the object mask by increasing the resolution through multiple upsampling modules and gradually get the mask by summing with the features in the backbone network. However, this refinement pathway does not fully consider the spatial information of the backbone features, and therefore, the segmentation results are not perfect. In this paper, the cross-stage and cross-resolution (CSCR) module is proposed for optimizing the segmentation effect. This module makes full use of the semantic information of high-level features and the spatial information of low-level features, and fuses them by skip connections to achieve a very accurate segmentation effect. Experiments were conducted on the VOT dataset, and the experimental results outperformed other excellent trackers and verified the effectiveness of the algorithm in this paper. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.3390/info13030147 | INFORMATION |
Keywords | DocType | Volume |
visual object tracking, refinement pathway, skip connection | Journal | 13 |
Issue | ISSN | Citations |
3 | 2078-2489 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Liang Xu | 1 | 0 | 0.68 |
| Shuli Cheng | 2 | 6 | 7.59 |
| Liejun Wang | 3 | 2 | 6.13 |