SRTrack: Self-Recovering Visual Tracking via Re-Detection

Visual object tracking has made significant progress through spatio-temporal context modeling. However, current approaches lack explicit spatial priors from historical predictions to guide localization, relying on feature similarity for attention computation. More critically, existing trackers implicitly assume reliable predictions throughout the sequence and lack specific mechanisms to detect and recover from tracking failures. To address these challenges, we propose SRTrack, a self-recovering tracking framework. For failure recovery, a tracking quality estimator monitors confidence at every frame and triggers a re-localization module to predict a coarse anchor on an extended search region. For robust localization, a trajectory-guided feature sampler leverages historical positions as geometric priors for feature aggregation, and a feature refinement head performs coarse-to-fine localization through iterative deformable attention with Gaussian gates. Experiments on eight benchmarks demonstrate that SRTrack achieves competitive results, with an AO of 80.9% on GOT-10k and an AUC of 74.4% on LaSOT, surpassing HIPTrack by 3.5% and 1.7%, respectively. The code and results will be made publicly available.