TempoTrack: Temporal Distribution Matching for Open-Vocabulary Multi-Object Tracking

Open-vocabulary multi-object tracking requires identity association for categories without task-specific identity supervision. Existing tracking methods retain appearance histories, yet prototypes or pooled similarities compress this history and can obscure how past observations support the current query. We propose TempoTrack, which represents each query–identity pair through the empirical distribution of its historical matching responses. Recent and long-term distributional evidence is combined by a candidate-specific temporal gate and candidate-relative scorer, while a moment-generating formulation provides a compact second-order parameterization without explicit covariance construction. TempoTrack introduces a lightweight, frame-causal association layer on top of frozen visual frontends and is trained using only Base identity supervision. On TAO, TempoTrack reaches 43.3 Novel AssocA on validation and 33.6 on test. These results support query-conditioned temporal distributions as a lightweight and effective representation of historical identity evidence for open-vocabulary association.