Multi-View In-Cabin Monitoring System for Public Transport Vehicles

Evgeny Gorelik; Kenny Dean Karrow; Fikret Sivrikaya; Sahin Albayrak; Christian Baumann

cs.CVJun 10, 2026

Multi-View In-Cabin Monitoring System for Public Transport Vehicles

Authors: Evgeny Gorelik, Kenny Dean Karrow, Fikret Sivrikaya, Sahin Albayrak, Christian Baumann

Organizations: GT-ARC, TU Berlin · Berlin, Germany · MAN Truck & Bus SE · Munich, Germany

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 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.

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Jun 11, 2026 · cs.CVJ/K move · Enter open · S save

PMOF: A Dataset and Benchmark for Passenger Monitoring Using Overhead Fisheye Cameras

Stella Katharina Wermuth, Qazi Arbab Ahmed, Klaus Neumann, Thorsten Jungeblut

Hochschule Bielefeld* · Hochschule Bielefeld · Bielefeld University*

Autonomous staff-free public transport requires reliable in-vehicle passenger monitoring. However, perception inside moving vehicles is challenged by confined spaces, variable illumination, motion-induced background variation, occlusion, and limited viewpoints. To mitigate these spatial constraints, ceiling-mounted fisheye cameras provide full-scene coverage from a single viewpoint. Yet existing public overhead fisheye datasets are recorded in static environments and do not capture the domain shift introduced by vehicle motion. To fill this gap, we introduce PMOF, Passenger Monitoring using Overhead Fisheye cameras, the first public dataset of top-view fisheye imagery captured inside a moving vehicle, comprising over 19k manually annotated frames. PMOF provides rotated bounding boxes, tracking identifiers, and action labels, supporting object detection, tracking, and action recognition. We benchmark PMOF using YOLO26m-obb models fine-tuned under multiple dataset configurations that combine PMOF with existing overhead fisheye datasets. Cross-domain fine-tuning with custom rotation-aware augmentation achieves 94.8% AP50 on PMOF and 96.5% AP50 on an unseen overhead fisheye dataset from a different domain. Our results highlight the domain gap between static and moving environments and show that incorporating PMOF improves detection performance and advances generalization beyond passenger monitoring to broader fisheye-based person detection tasks. The dataset and code are available at https://swermuth.github.io/pmof/.

Object Detection

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Abstract

Explore similar work

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