arXiv:2606.11739v1 Announce Type: cross Abstract: 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 m
The development of advanced AI models and sensing hardware is enabling more sophisticated in-cabin monitoring systems for safety and autonomy applications in public transport.
This dataset and system contribute to enhanced safety, efficiency, and the development of autonomous capabilities in public transportation, with implications for urban planning and public trust.
The availability of a comprehensive, multi-view dataset with synchronized RGB, depth, and LiDAR data, along with advanced pose estimation, provides a robust foundation for AI development in public transport monitoring.
- · Public transport operators
- · AI/computer vision researchers
- · Smart city developers
- · Automotive sensor manufacturers
- · Traditional surveillance vendors
- · Human-centric monitoring services
Improved passenger safety and operational efficiency within public transport vehicles through advanced AI monitoring.
Accelerated development and deployment of autonomous features in public transport, potentially leading to reduced labor costs and optimized routes.
Enhanced public acceptance and adoption of autonomous urban mobility solutions, shifting societal views on transit safety and privacy.
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Read at arXiv cs.AI