Transformer tracking with multi-scale dual-attention

Abstract

Abstract Transformer-based trackers greatly improve tracking success rate and precision rate. Attention mechanism in Transformer can fully explore the context information across successive frames. Nevertheless, it ignores the equally important local information and structured spatial information. And irrelevant regions may also affect the template features and search region features. In this work, a multi-scale feature fusion network is designed with box attention and instance attention in Encoder–Decoder architecture based on Transformer. After extracting features, the local information and structured spatial information is learnt by multi-scale box attention, and the global context information is explored by instance attention. Box attention samples grid features from the region of interest. Therefore, it effectively focuses on the region of interest (ROI) and avoids the influence of irrelevant regions in feature extraction. At the same time, instance attention can also pay attention to the context information across successive frames, and avoid falling into local optimum. The long-range feature dependencies are learned in this stage. Extensive experiments are conducted on six challenging tracking datasets to demonstrate the superiority of the proposed tracker MDTT, including UAV123, GOT-10k, LaSOT, VOT2018, TrackingNet, and NfS. In particular, the proposed tracker achieves AUC score of $$64.7 \% $$ 64.7 % on LaSOT, $$78.1 \%$$ 78.1 % on TrackingNet and precision score of $$89.2 \%$$ 89.2 % on UAV123, which outperforms the baseline and most recent advanced trackers.

Keywords

• Transformer
• Attention mechanism
• Context information
• Local information
• Feature fusion network