{"ID":2895670,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.08494","arxiv_id":"2507.08494","title":"One Graph to Track Them All: Dynamic GNNs for Single- and Multi-View Tracking","abstract":"This work presents a unified, fully differentiable model for multi-people tracking that learns to associate detections into trajectories without relying on pre-computed tracklets. The model builds a dynamic spatiotemporal graph that aggregates spatial, contextual, and temporal information, enabling seamless information propagation across entire sequences. To improve occlusion handling, the graph can also encode scene-specific information. We also introduce a new large-scale dataset with 25 partially overlapping views, detailed scene reconstructions, and extensive occlusions. Experiments show the model achieves state-of-the-art performance on public benchmarks and the new dataset, with flexibility across diverse conditions. Both the dataset and approach will be publicly released to advance research in multi-people tracking.","short_abstract":"This work presents a unified, fully differentiable model for multi-people tracking that learns to associate detections into trajectories without relying on pre-computed tracklets. The model builds a dynamic spatiotemporal graph that aggregates spatial, contextual, and temporal information, enabling seamless information...","url_abs":"https://arxiv.org/abs/2507.08494","url_pdf":"https://arxiv.org/pdf/2507.08494v2","authors":"[\"Martin Engilberge\",\"Ivan Vrkic\",\"Friedrich Wilke Grosche\",\"Julien Pilet\",\"Engin Turetken\",\"Pascal Fua\"]","published":"2025-07-11T11:17:25Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[\"Graph Neural Network\"]","has_code":false}