PRMTrack: Progressive Reliability Modeling for Satellite Video Object Tracking

Satellite video object tracking remains challenging due to tiny object sizes, substantial aspect-ratio variations, frequent occlusions, and interference from similar objects in complex backgrounds, which often lead to template-search representation mismatch, response ambiguity, and dynamic template contamination. To address these challenges, we propose PRMTrack, a Siamese network-based progressive reliability modeling framework for satellite video object tracking that improves tracking reliability across three consecutive stages: object representation, response recalibration, and template renewal. Specifically, we develop a Multi-Scale Sparse Perception Network that strengthens structural object representations through multi-scale feature modeling and sparse interactions, alleviating feature mismatch caused by aspect-ratio variations. We further design an Adaptive Dual-Branch Response Recalibration Network that learns branch-specific Gamma mappings according to the distinct distributions of classification and centerness responses, enhancing genuine object responses while suppressing distracting peaks from similar objects. Finally, we introduce an Object-Ratio-Guided Reliable Template Renewal Mechanism that evaluates scale consistency using the area ratio between the current object and historical template and adaptively controls template renewal, thereby reducing template contamination caused by occlusions and abnormal scale estimation. Extensive experiments on SatSOT, OOTB, and SV248S demonstrate strong tracking performance, with PRMTrack achieving 74.9% precision and a 53.0% success rate on SatSOT. Source code is available at: https://anonymous.4open.science/r/Track-SVOT.