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

• URL: http://arxiv.org/abs/2607.00369v1
• Date: Wed, 01 Jul 2026 03:09:12 GMT
• Title: SFDATrack: Generalized Source-Free Domain Adaptive Tracking Under Adverse Weather Conditions
• Abstract summary: SFDATrack is a source-free domain adaptive tracker for tracking objects in adverse weather conditions.<n>We show that SFDATrack achieves superior performance compared to state-of-the-art approaches.
• Score: 81.31561238030939
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Domain adaptive visual object tracking under adverse weather conditions has garnered significant attention in recent years. Despite the impressive performance, existing methods heavily rely on the large-scale video frames from both source and target domains, which is impractical under rigid resource constraints where source data is unavailable. To overcome this limitation, we propose SFDATrack, a generalized source-free domain adaptive tracker that merely leverages adverse weather samples from the target domain for robust state estimation. Specifically, SFDATrack first employs a mean-teacher backbone with Dual Interactive Mamba (DIM) blocks to distill the candidate target tokens that are resilient to weather variations from classified, augmented samples. Afterwards, we introduce a hyperspherical prototype projection (HPP) module to project these tokens onto multi-domain prototypes 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 conditions with powerful generalizability. Extensive experiments evaluated on various benchmarks demonstrate that SFDATrack achieves superior performance compared to state-of-the-art approaches. The code is available at https://github.com/watcherBR0/sfdatrack.

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