mlsanityData to 14 Oct 2026

Synthehicle: Multi-Vehicle Multi-Camera Tracking in Virtual Cities

arXiv 2208.14167 ↗First posted 30 Aug 2022cs.CVcited by 12

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Abstract (arXiv)

Smart City applications such as intelligent traffic routing or accident prevention rely on computer vision methods for exact vehicle localization and tracking. Due to the scarcity of accurately labeled data, detecting and tracking vehicles in 3D from multiple cameras proves challenging to explore. We present a massive synthetic dataset for multiple vehicle tracking and segmentation in multiple overlapping and non-overlapping camera views. Unlike existing datasets, which only provide tracking ground truth for 2D bounding boxes, our dataset additionally contains perfect labels for 3D bounding boxes in camera- and world coordinates, depth estimation, and instance, semantic and panoptic segmentation. The dataset consists of 17 hours of labeled video material, recorded from 340 cameras in 64 diverse day, rain, dawn, and night scenes, making it the most extensive dataset for multi-target multi-camera tracking so far. We provide baselines for detection, vehicle re-identification, and single- and multi-camera tracking. Code and data are publicly available.

Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Builds on 36 of its 69 references that are in the map. First posted on arXiv 30 Aug 2022.

Builds on

36 of 69 references.

References · 36

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24 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 4; 2024: 4; 2025: 3; 2026: 1.

12 papers.