Multi-View In-Cabin Monitoring System for Public Transport Vehicles
Provides a new dataset and tools for in-cabin perception in public transport, enabling research on occupant monitoring for safety and automation.
The paper introduces a multi-view in-cabin monitoring dataset for public transport with 9,136 synchronized RGB-D and LiDAR samples, along with calibration and pseudo-labeling pipelines for 3D human pose and bounding box estimation. Benchmarking of multi-view 3D detection models shows feasibility for small-scale training.
We introduce a multi-view in-cabin monitoring dataset for public transportation with synchronized RGB and depth images from four inward-facing cameras and a rotating LiDAR covering the vehicle interior of a digitalized and partly automated German city bus. The dataset contains 9.136 synchronized samples with annotations and is accompanied by a calibration and pseudo-labeling pipeline that generates 3D human pose estimates and oriented 3D bounding boxes for occupants. We further provide a nuScenes-format conversion and benchmark representative multi-view 3D detection models (e.g., Lift-Splat-Shoot and BEVFusion), supporting comparative evaluation and small-scale training of multi-view in-cabin perception models. The dataset and tools are available at https://github.com/EvgenyGorelik/multiview_incabin_dataset.