Multiscale Collaborative Attention Network for Robust Multiobject Tracking in Satellite Video

Satellite video has revolutionized Earth observation, offering unprecedented capabilities for automated object tracking. However, multi-object tracking (MOT) in satellite videos remains highly challenging due to complex backgrounds, extremely low signal-to-noise ratios (SNR), and sub-pixel-scale targets. Although existing MOT methods achieve satisfactory performance under conventional conditions, their effectiveness declines significantly when applied to satellite imagery. To address these challenges, we propose a novel MOT framework tailored specifically for remote sensing videos, termed the Multi-scale Collaborative Attention Network (MCANet), which explicitly incorporates temporal and spatial contextual information to enhance tracking performance. Specifically, we design a Collaborative Frame Enhancement Module (CFEM), which improves small-scale target detection by utilizing cross-frame spatiotemporal feature compensation. This module robustly suppresses background interference and enhances weak target features in low-SNR environments. Additionally, we propose a Multi-scale Temporal Attention Motion Capture Module (MTAMC) that integrates multi-layer spatiotemporal attention mechanisms to effectively capture complex motion patterns, significantly improving target association and trajectory continuity under low temporal resolution conditions. Extensive experiments on representative satellite imagery datasets confirm that MCANet substantially outperforms existing state-of-the-art methods, achieving a MOTA score of 69.1% and an IDF1 score of 77.6% on the VISO dataset. These results highlight the effectiveness, robustness, and practical applicability of MCANet in remote sensing MOT scenarios.