PIFTrack: Point-of-Interest Flows for Multiobject Tracking in Satellite Videos

Data annotation is extremely difficult due to the satellite imaging conditions, under which the targets are usually small, obscured, and scattered. Therefore, satellite video data annotation inevitably has noise and errors. Moreover, the irregular acceleration, sudden turns, and stops of the target make prediction and trajectory maintenance highly challenging. In this study, we propose the point-of-interest flow track (PIFTrack) to address the aforementioned challenges. PIFTrack improves tracking accuracy by modeling target uncertainty distributions and nonlinear motion patterns while leveraging the spatial inclusion relationships of points of interest (PoIs) across consecutive frames. Specifically, we eliminate the rigid Dirac-based labeling assumption by employing a set of PoIs to model the spatial probability distribution of the target. PoIs enable the model to infer optimal outputs in the vicinity of annotations, thereby improving robustness to annotation errors. Second, to capture the real motion transfer patterns of targets in the data, we introduce a diffusion-based ordinary differential equation (ODE) model. Ultimately, we alleviate the impact of tiny object localization drifts on association results by exploiting the inclusion relationship between PoIs. PIFTrack has been extensively evaluated on the VISO, AIR-MOT, CGSTL, and VSMB datasets, exhibiting competitive performance relative to contemporary studies. Our code is open source and available at https://github.com/softwarePupil/PIFTrack