FeatTracker: Short- and Long-Range Temporal Feature Consistency for Robust Underwater Object Tracking

Existing underwater object tracking (UOT) methods can bebroadly divided into two categories: frame-level trackers and video-leveltrackers. Frame-level trackers process each frame independently, usingenhancement or adaptation techniques to handle underwater distortions.However, this often results in inconsistent feature styles and weakenedtemporal correlations. Video-level trackers attempt to improve temporalconsistency by modeling dependencies across frames through autoregres-sive mechanisms. Despite this, they still struggle with persistent featuredegradation and tracking drift caused by challenging underwater condi-tions. To overcome these limitations, we propose FeatTracker, a feature-level tracking framework that enforces both short- and long-range tem-poral feature consistency, effectively preserving semantic integrity andensuring temporal stability for robust underwater object tracking. Theframework integrates two key innovations: the Short-Range TemporalFeature Consistency module, which combines diffusion-like feature en-hancement with a dynamic memory pool to mitigate degradation andpreserve local temporal coherence; and the Long-Range Temporal Fea-ture Consistency module, which leverages wavelet decomposition to sepa-rate stable structural components from transient details within historicaltokens, effectively preventing the loss of trajectory details. Extensive ex-periments demonstrate that FeatTracker achieves state-of-the-art perfor-mance across four UOT benchmarks. Our code and models are availableat https://github.com/fishgfish/FeatTracker.