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EANTrack: An Efficient Attention Network for Visual Tracking
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AI
DOI：10.1109/TASE.2023.3319676.png)
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Abstract
En 中文
Recently, Siamese trackers have gained widespread attention in visual tracking due to their exceptional performance. However, many trackers still suffer from limitations in challenging scenarios, such as fast motion and scale variation, which hinder the full exploitation of target features. Consequently, the accuracy and efficiency of the trackers are limited. Therefore, this paper proposes an efficient attention network, called EAN, to improve tracking performance. The EAN comprises three primary components, namely a Transformer-s subnetwork, a Transformer-t subnetwork, and a Feature-Fused Attention Module (FFAM). The designed Transformer-s and Transformer-t subnetworks adopt complementary structures and functions to fully integrate and emphasize the relevant feature information, including channel and spatial features. The FFAM is responsible for fusing the multi-level features from both subnetworks, which establishes the global dependencies between the templates and search regions and enhances the discriminative power of the model. To further improve the tracking accuracy, a novel Feature-Aware Attention Module (FAAM) is introduced into the tracking prediction head to enhance the feature representation capability of the model. Finally, we propose an efficient EANTrack tracker based on EAN for robust tracking in complex scenarios, which exhibits significant advantages in challenging attributes. Experimental results on multiple benchmarks indicate that our approach achieves remarkable tracking performance with a real-time running speed of 55.6fps.
Keywords:
Video sequences
Visual tracking
challenging scenarios
target features
attention network
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