ADATrack: Anisotropic Diffusion Aware Multi-Object Tracker for Satellite Video

Multi-object tracking in satellite video requires preserving identities when limited appearance cues and uncertain localization make nearby targets difficult to distinguish. This motivates a central question: how can historical evidence, current observations, and motion direction jointly shape the spatial support used for association? We propose ADATrack, an anisotropic diffusion aware tracking framework that makes this support a recurrent state for each track. Its central design couples cross-frame evidence accumulation directly to association scoring through an explicit field evolution mechanism. A learned encoder predicts an anchor and evolution parameters from track history. The retained field is warped to the predicted anchor, then updated through anisotropic diffusion, decay, and an anchor-centered source modulated by current detector responses. Warping accounts for displacement, directional diffusion spreads support preferentially along the historical motion axis, and the source injects current visual evidence. Normalized field responses sampled at detection centers define the costs for one-to-one association. After matching, the field is aligned with the observation when available and carried into the next frame. Anchor and association supervision jointly train the encoder through this recurrence. On VISO and SAT-MTB, ADATrack attains 67.1 and 60.5 HOTA, respectively, while achieving the highest IDF1 and fewest identity switches among the compared trackers.