Cross-View Urban Traffic Dataset: Drone-Supervised Ground Truth for Monocular Bird's-Eye View Localization
arXiv:2606.07708v1 Announce Type: cross Abstract: We introduce a dataset and benchmark for cross-view urban traffic perception built from synchronized ego-centric bicycle videos and aerial drone videos recorded at real urban intersections. The benchmark targets two linked tasks: cross-view identity matching between street-view and drone-view object tracks, and ego-to-bird's-eye-view prediction using aerial supervision. In contrast to prior urban driving and V2X datasets, our benchmark provides identity-level alignment across radically different viewpoints together with standardized evaluation,
The proliferation of drone technology and advanced computer vision techniques allows for the creation of sophisticated datasets for AI model training.
This new dataset provides crucial cross-view, identity-level alignment for urban traffic perception, which is vital for developing robust autonomous driving and drone navigation systems.
AI models can now be trained with higher fidelity, multi-perspective data for urban environments, improving bird's-eye view localization and object tracking in complex scenarios.
- · Autonomous vehicle developers
- · Urban planning technologists
- · Drone delivery companies
- · Computer vision researchers
- · Companies relying on single-view or less robust perception systems for urban aut
Improved accuracy and safety of autonomous vehicles and urban drones, particularly in complex intersections.
Faster development and deployment of urban autonomous mobility solutions, leading to new logistical efficiencies and services.
Enhanced urban infrastructure management through precise, real-time traffic flow analysis and predictive modeling using AI.
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Read at arXiv cs.AI