Multi-View In-Cabin Monitoring System for Public Transport Vehicles
This work addresses the lack of synchronized, well-annotated multi-view RGB-D and LiDAR datasets in public transit cabins, which has hindered research on occupant state understanding and 3D perception. To bridge this gap, we present the first multimodal, synchronized dataset specifically designed for bus interiors, integrating four RGB-D cameras and a rotating LiDAR sensor. The dataset comprises 9,136 meticulously annotated samples and is accompanied by a complete pipeline including sensor calibration, end-to-end pseudo-label generation, and conversion to the nuScenes format. It enables training and evaluation of state-of-the-art multi-view 3D detection models such as Lift-Splat-Shoot and BEVFusion, significantly advancing research and applications in high-precision 3D human pose estimation and oriented bounding box prediction within cabin environments.