Addressing single object tracking in satellite imagery through prompt-engineered solutions
This addresses robust tracking for satellite imagery in remote sensing, but it is incremental as it adapts existing models to a specific domain.
The paper tackled single object tracking in satellite imagery by proposing a training-free point-based method using prompt engineering with SAM and TAPIR, achieving validation on the VISO dataset as a significant advancement for remote sensing.
Object tracking in satellite videos remains a complex endeavor in remote sensing due to the intricate and dynamic nature of satellite imagery. Existing state-of-the-art trackers in computer vision integrate sophisticated architectures, attention mechanisms, and multi-modal fusion to enhance tracking accuracy across diverse environments. However, the challenges posed by satellite imagery, such as background variations, atmospheric disturbances, and low-resolution object delineation, significantly impede the precision and reliability of traditional Single Object Tracking (SOT) techniques. Our study delves into these challenges and proposes prompt engineering methodologies, leveraging the Segment Anything Model (SAM) and TAPIR (Tracking Any Point with per-frame Initialization and temporal Refinement), to create a training-free point-based tracking method for small-scale objects on satellite videos. Experiments on the VISO dataset validate our strategy, marking a significant advancement in robust tracking solutions tailored for satellite imagery in remote sensing applications.