| Popis: |
The tracker based on the Siamese network describes the object-tracking task as a similarity-matching problem. The Siamese network is the current mainstream model. It achieves similarity learning by applying correlation filters to the target and search branches’ convolution features. However, because the correlation operation uses a local linear matching process, semantic information is lost, and it is simple to run into the issue of local optimality. Transformer Tracking has recently been proposed using an attention-based feature fusion network instead of the previous correlation operation to achieve excellent results. However, it only uses limited feature space resolution. Because of the limitations of the Transformer module, the network’s convergence is also very slow. We propose Deformable Transformer Tracking (D-TransT) which employs a deformable attention module that pre-filters for prominent key elements among all feature map pixels using a small set of sampling locations, and this module can be naturally extensible to aggregating multi-scale features. D-TransT can have faster convergence and better prediction than Transformer Tracking. D-TransT improves the convergence speed by 29.4% and achieves 65.6%, 73.3%, and 69.1% in AUC, PNorm and P, respectively. The experimental results demonstrate that the proposed tracker performs better than the most state-of-the-art algorithm. |
