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Lightweight Target-Aware Attention Learning Network-Based Target Tracking Method
| Content Provider | MDPI |
|---|---|
| Author | Zhao, Yanchun Zhang, Jiapeng Duan, Rui Li, Fusheng Zhang, Huanlong |
| Copyright Year | 2022 |
| Description | Siamese network trackers based on pre-trained depth features have achieved good performance in recent years. However, the pre-trained depth features are trained in advance on large-scale datasets, which contain feature information of a large number of objects. There may be a pair of interference and redundant information for a single tracking target. To learn a more accurate target feature information, this paper proposes a lightweight target-aware attention learning network to learn the most effective channel features of the target online. The lightweight network uses a designed attention learning loss function to learn a series of channel features with weights online with no complex parameters. Compared with the pre-trained features, the channel features with weights can represent the target more accurately. Finally, the lightweight target-aware attention learning network is unified into a Siamese tracking network framework to implement target tracking effectively. Experiments on several datasets demonstrate that the tracker proposed in this paper has good performance. |
| Starting Page | 2299 |
| e-ISSN | 22277390 |
| DOI | 10.3390/math10132299 |
| Journal | Mathematics |
| Issue Number | 13 |
| Volume Number | 10 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-06-30 |
| Access Restriction | Open |
| Subject Keyword | Mathematics Industrial Engineering Target Features Siamese Trackers Lightweight Network Target Tracking |
| Content Type | Text |
| Resource Type | Article |
