SFDATrack: Generalized Source-Free Domain Adaptive Tracking Under Adverse Weather Conditions

Domain adaptive visual object tracking under adverse weatherconditions has garnered significant attention in recent years. Despitethe impressive performance, existing methods heavily rely on the large-scale video frames from both source and target domains, which is im-practical under rigid resource constraints where source data is unavail-able. To overcome this limitation, we propose SFDATrack, a general-ized source-free domain adaptive tracker that merely leverages adverseweather samples from the target domain for robust state estimation.Specifically, SFDATrack first employs a mean-teacher backbone withDual Interactive Mamba (DIM) blocks to distill the candidate targettokens that are resilient to weather variations from classified, augmentedsamples. Afterwards, we introduce a hyperspherical prototype projec-tion (HPP) module to project these tokens onto multi-domain proto-types within a latent hyperspherical space. By enforcing both domain-specific and domain-invariant properties of the multi-domain prototypes,SFDATrack can be seamlessly adapted to diverse weather conditionswith powerful generalizability. Extensive experiments evaluated on vari-ous benchmarks demonstrate that SFDATrack achieves superior perfor-mance compared to state-of-the-art approaches. The code is available athttps://github.com/watcherBR0/sfdatrack.