HieDG: A Hierarchical Discrete Geometry-Guided Framework for Multi-Animal Tracking
2026-07-01 • Computer Vision and Pattern Recognition
Computer Vision and Pattern Recognition
AI summaryⓘ
The authors address the challenge of tracking multiple animals, which is hard because animals look similar and move irregularly. They found that existing methods struggle with unstable geometric data that hurts tracking accuracy. To fix this, they created HieDG, a system that converts continuous movement data into stable, discrete tokens using a two-step coding process. This helps the tracker keep better track of each animal's identity. Their tests show improved tracking performance on animal and general multi-object datasets.
Multi-animal trackingGeometric embeddingsQuery-based trackingDiscrete representationsResidual codebookIdentity associationHOTAIDF1Cross-frame attentionMulti-object tracking
Authors
Chenxun Deng, Zhongde Zhang, Ye Yuan, Chengyang Zhang, Yifan Zhang, Bohao Chen, Hongying Yan, Hang Zhou, Hua Han, Xi Chen
Abstract
Multi-animal tracking (MAT) is critical for wildlife monitoring and behavioral analysis, yet remains challenging due to uniform appearance, high density, and irregular motion. Existing methods typically follow heuristic- or query-based paradigms: the former relies on handcrafted geometric associations without end-to-end optimization, whereas the latter enables joint optimization but relies heavily on appearance embeddings. In such conditions, continuous geometric embeddings can be unstable, as small coordinate perturbations may disproportionately alter cross-frame attention weights, degrading identity association performance. To address this limitation, we propose HieDG, a Hierarchical Discrete Geometry-guided tracking framework that reformulates geometric dynamics as structured discrete representations within a query-based tracker. Instead of directly using raw geometric signals, HieDG employs a two-stage residual codebook to discretize position, scale, and velocity cues, transforming unstable continuous geometry into structured, stable discrete tokens. These tokens are aligned with visual embeddings and integrated into the tracking queries to enhance identity consistency. Extensive experiments on animal-specific benchmarks (AnimalTrack, BFT, and BuckTales) demonstrate state-of-the-art association performance with significant improvements in HOTA, AssA, and IDF1. Additional evaluations on generic multi-object tracking benchmarks, including DanceTrack and SportsMOT, show competitive performance, indicating the broader applicability of discretized geometric modeling beyond animal-specific scenarios.