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Geometrical Relation Prediction Transformer for UAV-Ground Visual Tracking
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DOI：10.1007/s12559-026-10608-4.png)
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Abstract
En 中文
UAV-Ground visual tracking is to achieve robust tracking by leveraging the discriminative information from both UAV and ground views. The existing approach uses a multi-view collaborative model to associate and fuse target features from different views by calculating the appearance similarity. However, it fails in challenging scenarios due to cross-view spatial misalignment caused by ignored geometric relations. To handle this problem, we propose a robust UAV-Ground tracker based on the novel Geometric Relation Prediction Transformer (GRPT), which leverages the coordinate offset of the target between two views to achieve accurate collaborative modeling. Moreover, we design a SRA strategy to adaptively correct the location of search regions for cross-view spatial alignment. We evaluate our method on public dataset UGVT, achieving 82.5% PR in UAV view, improving the baseline by 3.9%.
Keywords:
UAV-ground visual tracking
Geometrical relation
Transformer
Spatial alignment
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