Unified People Tracking with Graph Neural Networks

• URL: http://arxiv.org/abs/2507.08494v1
• Date: Fri, 11 Jul 2025 11:17:25 GMT
• Title: Unified People Tracking with Graph Neural Networks
• Authors: Martin Engilberge, Ivan Vrkic, Friedrich Wilke Grosche, Julien Pilet, Engin Turetken, Pascal Fua,
• Abstract summary: We present a unified, fully differentiable model for multi-people tracking that learns to associate detections into trajectories.<n>The model builds a dynamic graph that aggregates spatial, contextual, and temporal information.<n>We also introduce a new scale dataset with 25 partially overlapping views, detailed scene reconstructions, and extensive occlusions.
• Score: 39.22185669123208
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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