CVAC-Net: Cross-View Alignment and Adaptive Collaboration Network for Aerial–Ground Visual Tracking

Aerial–ground visual tracking facilitates dynamic remote sensing and scene monitoring by integrating complementary observations from unmanned aerial vehicle (UAV) and ground cameras, allowing one view to compensate when the target is poorly observed or temporarily invisible in the other. However, substantial cross-view variations in target appearance, scale, and geometry make it difficult to establish reliable target correspondences and effectively fuse complementary cues. To address these challenges, we propose CVAC-Net, a Cross-View Alignment and Adaptive Collaboration Network for aerial–ground visual tracking. Specifically, CVAC-Net uses cross-view contrastive learning to reduce semantic differences and a learned spatial offset to adjust the relative positions of aerial- and ground-view target features within their normalized search crops. Next, it uses the similarity between the main-view template and the aligned auxiliary-view search features to generate an importance map that reweights redundant and target-irrelevant regions, thereby improving the reliability of feature aggregation. Finally, its view-specific branches select and weight the top-K encoder outputs to integrate complementary cross-view features. Extensive experiments on the UAV–ground visual tracking (UGVT) benchmark show that CVAC-Net improves the baseline by 3.2, 3.1, and 1.7 percentage points in success rate (SR), normalized precision rate (NPR), and precision rate (PR), respectively. We further evaluate CVAC-Net using a two-dimensional (2D) single-object tracking protocol derived from Griffin using its predefined training and validation splits, providing supplementary validation on a simulated aerial–ground distribution.