HieDG: A Hierarchical Discrete Geometry-Guided Framework for Multi-Animal Tracking

Multi-animal tracking (MAT) is critical for wildlife monitor-ing and behavioral analysis, yet remains challenging due to uniform ap-pearance, high density, and irregular motion. Existing methods typicallyfollow heuristic- or query-based paradigms: the former relies on hand-crafted geometric associations without end-to-end optimization, whereasthe latter enables joint optimization but relies heavily on appearanceembeddings. In such conditions, continuous geometric embeddings canbe unstable, as small coordinate perturbations may disproportionatelyalter cross-frame attention weights, degrading identity association perfor-mance. To address this limitation, we propose HieDG, a HierarchicalDiscrete Geometry-guided tracking framework that reformulates geo-metric dynamics as structured discrete representations within a query-based tracker. Instead of directly using raw geometric signals, HieDGemploys a two-stage residual codebook to discretize position, scale, andvelocity cues, transforming unstable continuous geometry into struc-tured, stable discrete tokens. These tokens are aligned with visual em-beddings and integrated into the tracking queries to enhance identityconsistency. Extensive experiments on animal-specific benchmarks (Ani-malTrack, BFT, and BuckTales) demonstrate state-of-the-art associationperformance with significant improvements in HOTA, AssA, and IDF1.Additional evaluations on generic multi-object tracking benchmarks, in-cluding DanceTrack and SportsMOT, show competitive performance,indicating the broader applicability of discretized geometric modelingbeyond animal-specific scenarios.