Name
Abstract | ||
|---|---|---|
Tracking-by-detection is a very popular framework for single-object tracking that attempts to search the target object within a local search window for each frame. Although such a local search mechanism works well on simple videos, however, it makes the trackers sensitive to extremely challenging scenarios, such as heavy occlusion and fast motion. In this article, we propose a novel and general target-aware attention mechanism (termed TANet) and integrate it with a tracking-by-detection framework to conduct joint local and global search for robust tracking. Specifically, we extract the features of the target object patch and continuous video frames; then, we concatenate and feed them into a decoder network to generate target-aware global attention maps. More importantly, we resort to adversarial training for better attention prediction. The appearance and motion discriminator networks are designed to ensure its consistency in spatial and temporal views. In the tracking procedure, we integrate target-aware attention with multiple trackers by exploring candidate search regions for robust tracking. Extensive experiments on both short- and long-term tracking benchmark datasets all validated the effectiveness of our algorithm. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TNNLS.2021.3083933 | IEEE Transactions on Neural Networks and Learning Systems |
Keywords | DocType | Volume |
Neural Networks, Computer,Video Recording,Algorithms | Journal | 33 |
Issue | ISSN | Citations |
11 | 2162-237X | 0 |
PageRank | References | Authors |
0.34 | 29 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiao Wang | 1 | 3 | 1.04 |
| Jin Tang | 2 | 322 | 62.02 |
| Bin Luo | 3 | 802 | 107.57 |
| Yaowei Wang | 4 | 134 | 29.62 |
| Yonghong Tian | 5 | 1057 | 102.81 |
| Feng Wu | 6 | 3635 | 295.09 |