Single Object Tracking in Satellite Videos: A Correlation Filter-Based Dual-Flow Tracker

Satellite video (SV) can acquire rich spatiotemporal information on the earth. Single object tracking (SOT) in SVs enables the continuous acquisition of the position and range of a specific object, expanding the field of remote-sensing applications. In SVs, objects are small with limited features and vulnerable to tracking drift. In this article, a correlation filter based dual-flow (DF) tracker is proposed to explore how the hybridization of spatial–spectral feature fusion and motion model can boost tracking. To represent small objects, the DF adaptively fuses complementary features using a state-aware indicator in feature flow. In motion flow, the indicator perceives the confidence of the feature flow. A dual-mode prediction model is then constructed to simulate the object's motion pattern and cooperate linear and nonlinear motion patterns to implement SOT in SVs. The ablation experiments demonstrate that the DF contributes to tracking. Experimental comparisons on 14 real SVs captured by the Jilin-1 satellite constellation show that DF achieves optimal performance with an area under the curve of 0.912 in the precision plot, 0.700 in the success plot, and a speed of 155.2 frames per second. This work would encourage the development of remote-sensing ground surveillance.