Autonomous driving paper index
Geometrical Relation Prediction Transformer for UAV-Ground Visual Tracking
One-line summary
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.
Engineering notes
Key topics: autonomous driving, prediction. See the paper for implementation details and experimental results.
Chinese explanation / 中文解读
中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。
Original abstract
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%.
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