Online Background Discriminative Learning for Satellite Video Object Tracking

Satellite video object tracking is a critical task in satellite video analysis, focused on locating targets within the imagery. Current research in satellite video single-object tracking primarily centers around traditional correlation filter-based methods and deep learning techniques based on Siamese networks. However, these methods encounter challenges, such as template inconsistency due to target appearance variations and imprecise object tracking caused by target–background similarities. In this article, we introduce a novel approach called satellite video object tracking Siamese network with online background discriminative learning and bounding box optimization (SiamOBR) to address these issues. Building upon the baseline twin network framework, our model leverages an online background discriminative learning module to initialize using input satellite video frames. This module improves the network’s ability to distinguish the target by dynamically updating the template, effectively addressing target deformations resulting from motion. To tackle inaccuracies in bounding box estimation, we introduce a bounding box optimization module that refines tracking results obtained from the baseline tracker, further enhancing tracking accuracy. During network training, we employ probability regression in place of confidence regression and utilize a cross-entropy loss function to rectify labeling errors for small targets where the labeled box’s center point may not align perfectly with the object. We conducted quantitative experiments on real-world satellite video datasets, demonstrating that the SiamOBR framework outperforms existing satellite video object tracking models, showcasing its effectiveness in this domain.