SpecHyper-Track: Relation-Specific Spectral Hypergraph Reasoning for Online Multi-Object Tracking

Relational reasoning methods often assume that all interactions share identical propagation behaviors, while real-world systems contain relations with different semantic and spectral characteristics. Online multi-object tracking (MOT) provides a challenging instance of this problem, where cooperative motion patterns and identity-conflicting interactions coexist. We introduce SpecHyper-Track, a relation-specific spectral hypergraph framework with stage-aware frequency routing for identity-preserving association. The framework constructs semantic hypergraphs with homogeneous and heterogeneous hyperedges to model cooperative and competitive relations, respectively, enabling relation-dependent spectral propagation. Cooperative relations are enhanced through low-frequency smoothing, whereas competitive relations preserve identity-sensitive variations through high-frequency responses. Leveraging tight‑framelet decomposition, we derive complementary frequency‑aware representations adaptively dispatched based on trajectory status: high-frequency features improve discrimination for established trajectories, while fused low- and high-frequency features provide stable context for newly initialized trajectories. The proposed framework couples semantic relational modeling, spectral representation learning, and state-aware association in a unified manner. Evaluations on MOT16, MOT17 and DanceTrack yield substantial improvements on association‑oriented metrics including HOTA, IDF1 and AssA in crowded interactive scenarios. Code is available at https://anonymous.4open.science/r/SpecHyper-Track-1869/.