Researchers Release Multi-View In-Cabin Monitoring Dataset for Public Transport Vehicles
Researchers have published a multi-view in-cabin monitoring dataset for public transportation, featuring synchronized RGB, depth, and LiDAR data from a German city bus. The dataset includes 9,136 annotated samples with 3D human pose estimates and bounding boxes for passengers, along with benchmarking tools for detection models. It aims to advance perception systems for automated and digitalized public transit vehicles.
A research team has introduced a publicly available dataset designed to support in-cabin monitoring of public transportation vehicles, specifically a partly automated German city bus. The dataset comprises 9,136 synchronized samples captured from four inward-facing cameras providing RGB and depth imagery, supplemented by a rotating LiDAR covering the vehicle interior. A calibration and pseudo-labeling pipeline accompanies the data, automatically generating 3D human pose estimates and oriented 3D bounding boxes for vehicle occupants. The dataset is provided in nuScenes format and includes benchmarks for representative multi-view 3D detection models, including Lift-Splat-Shoot and BEVFusion, enabling comparative evaluation. The work has been submitted to ICDM 2026 and the dataset and associated tools are publicly accessible via the provided repository link.
What's missing
The pseudo-labeling pipeline's error rates and the degree of manual annotation verification are not described in the abstract, leaving the ground-truth quality somewhat uncertain. The scope of generalizability beyond the single German city bus platform is also unaddressed.
What different sources said
- arXiv cs.AICenter
Multi-View In-Cabin Monitoring System for Public Transport Vehicles
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