STAR: A Unified Spatiotemporal Fusion Framework for Satellite Video Object Tracking

Satellite video object tracking (SVOT) delivers comprehensive spatiotemporal insights for Earth surface observation, yet existing SVOT methods confront several critical challenges including data scarcity, modality restrictions, paradigm gaps, and underutilization of multidimensional features, sealing the performance ceiling. This study proposes STAR, a unified spatiotemporal fusion framework for satellite video object tracking, mitigating these issues. To optimize satellite video scenes, STAR first introduces a scene enhancement module for generating enhanced multi-modal representations. Then, the extraction-correlation-adaptation module is designed, incorporating a multi-modal hierarchical Transformer architecture with local and unified relation modeling, which jointly achieves feature extraction, relation learning, and domain adaptation. Additionally, the temporal decoding structure is introduced to integrate deep temporal features via attention propagation. Finally, the inertial navigation module models physical temporal features, including an awareness selector to assess the tracking confidence-uncertainty and an inertial navigation scheme to manage anomalous interferences and continuous trajectory. Inspired by the prompt learning pattern, STAR introduces a minimal number of tunable parameters yet achieves competitive performance across various SVOT benchmarks. Implementation details and evaluation results will be available at: https://github.com/YZCU/STAR.