Hypergraph-State Collaborative Reasoning for Multi-Object Tracking

2026-04-14

AI Summary

This work addresses the vulnerability of existing multi-object tracking methods to trajectory fragmentation under noise and occlusion. To this end, the authors propose HyperSSM, a novel framework that uniquely integrates dynamic hypergraphs with state space models. Specifically, dynamic hypergraphs are employed to capture spatial associations among multiple targets, while state space models enforce temporal consistency; their joint inference enables collaborative optimization of motion states. Evaluated on four standard benchmarks—MOT17, MOT20, DanceTrack, and SportsMOT—the proposed method achieves state-of-the-art performance, demonstrating significantly enhanced robustness and trajectory continuity in complex, challenging scenarios.

Technology Category

• Computer Vision: Motion & Tracking
• Intelligent Robots: State Estimation
• Machine Learning: Multi-instance/Multi-view Learning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Motion reasoning serves as the cornerstone of multi-object tracking (MOT), as it enables consistent association of targets across frames. However, existing motion estimation approaches face two major limitations: (1) instability caused by noisy or probabilistic predictions, and (2) vulnerability under occlusion, where trajectories often fragment once visual cues disappear. To overcome these issues, we propose a collaborative reasoning framework that enhances motion estimation through joint inference among multiple correlated objects. By allowing objects with similar motion states to mutually constrain and refine each other, our framework stabilizes noisy trajectories and infers plausible motion continuity even when target is occluded. To realize this concept, we design HyperSSM, an architecture that integrates Hypergraph computation and a State Space Model (SSM) for unified spatial-temporal reasoning. The Hypergraph module captures spatial motion correlations through dynamic hyperedges, while the SSM enforces temporal smoothness via structured state transitions. This synergistic design enables simultaneous optimization of spatial consensus and temporal coherence, resulting in robust and stable motion estimation. Extensive experiments on four mainstream and diverse benchmarks(MOT17, MOT20, DanceTrack, and SportsMOT) covering various motion patterns and scene complexities, demonstrate that our approach achieves state-of-the-art performance across a wide range of tracking scenarios.

Problem

• multi-object tracking
• motion estimation
• occlusion
• trajectory fragmentation
• noisy prediction

Innovation

• Hypergraph
• State Space Model
• Collaborative Reasoning
• Multi-Object Tracking
• Motion Estimation