STTATrack: Enhancing One-Stream Single Object Tracking via Score Temporal Token Attention

One-stream transformer-based trackers have shown remarkable success in single object tracking by jointly performing feature extraction and relation modeling. However, the update of temporal context, often propagated via temporal tokens, typically relies on general self-attention mechanisms within the transformer backbone. This paper introduces STTATrack, a novel framework that enhances one-stream tracking by explicitly leveraging the immediate spatial certainty from the current frame’s prediction score map to refine these propagated temporal query tokens. Our core contribution, the Score Temporal Token Attention (STTA) module, generates an embedding from the score map and employs a dual attention mechanism to facilitate bidirectional information flow between this spatial certainty embedding and the existing temporal tokens. This targeted refinement allows temporal tokens to be dynamically adapted based on the most current and spatially precise evidence, leading to improved adaptability and temporal consistency. STTATrack builds upon the ODTrack architecture and demonstrates significant performance improvements on the challenging GOT10k, OTB and UAV123 benchmarks, underscoring the efficacy of explicitly integrating current-frame spatial certainty into the temporal refinement loop.