Transformer Tracking

• URL: http://arxiv.org/abs/2103.15436v1
• Date: Mon, 29 Mar 2021 09:06:55 GMT
• Title: Transformer Tracking
• Authors: Xin Chen, Bin Yan, Jiawen Zhu, Dong Wang, Xiaoyun Yang and Huchuan Lu
• Abstract summary: Correlation acts as a critical role in the tracking field, especially in popular Siamese-based trackers. This work presents a novel attention-based feature fusion network, which effectively combines the template and search region features solely using attention. Experiments show that our TransT achieves very promising results on six challenging datasets.
• Score: 76.96796612225295
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Correlation acts as a critical role in the tracking field, especially in recent popular Siamese-based trackers. The correlation operation is a simple fusion manner to consider the similarity between the template and the search region. However, the correlation operation itself is a local linear matching process, leading to lose semantic information and fall into local optimum easily, which may be the bottleneck of designing high-accuracy tracking algorithms. Is there any better feature fusion method than correlation? To address this issue, inspired by Transformer, this work presents a novel attention-based feature fusion network, which effectively combines the template and search region features solely using attention. Specifically, the proposed method includes an ego-context augment module based on self-attention and a cross-feature augment module based on cross-attention. Finally, we present a Transformer tracking (named TransT) method based on the Siamese-like feature extraction backbone, the designed attention-based fusion mechanism, and the classification and regression head. Experiments show that our TransT achieves very promising results on six challenging datasets, especially on large-scale LaSOT, TrackingNet, and GOT-10k benchmarks. Our tracker runs at approximatively 50 fps on GPU. Code and models are available at https://github.com/chenxin-dlut/TransT.

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