ReliTrack: Pseudo-State Reliability Learning for Self-Supervised Visual Tracking

Self-supervised visual tracking reduces reliance on dense bounding-box annotations by deriving training signals from unlabeled videos. A key challenge, however, is that model-generated target states are not equally reliable: inaccurate pseudo-states can corrupt the target context used for subsequent self-supervised optimization. To address this issue, we propose ReliTrack, a reliability-aware self-supervised tracking method designed to mitigate pseudo-state contamination. ReliTrack first generates a forward pseudo-trajectory and estimates its reliability from the sharpness of the corresponding localization responses. Concentrated responses indicate confident target localization, whereas flat or ambiguous responses suggest unreliable pseudo-states. The resulting frame-level estimates are aggregated into a trajectory-level reliability score, which is converted into a conservative soft weight to regulate backward localization learning. This weighting suppresses contaminated supervision without discarding training samples. The reliability estimation requires neither additional annotations nor auxiliary networks and introduces no inference-time computation. Experiments demonstrate consistent improvements in tracking performance, while further analysis shows that ReliTrack shifts the effective training signal toward higher-quality pseudo-trajectories and reduces the contribution of inaccurate ones.