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/.

翻译：全自主无人值守公共交通需要可靠的车辆内部乘客监测。然而，移动车辆内部的感知面临空间受限、光照变化、运动引起的背景波动、遮挡以及视角有限等挑战。为缓解这些空间限制，车顶安装的鱼眼摄像头可提供单一视角下的全景覆盖。然而现有的公开顶置鱼眼数据集均采集于静态环境，未捕捉车辆运动引入的域偏移。为填补这一空白，我们提出PMOF（Passenger Monitoring using Overhead Fisheye cameras），这是首个在移动车辆内部采集的顶视鱼眼图像公开数据集，包含超过1.9万帧人工标注数据。PMOF提供旋转边界框、跟踪标识及行为标签，支持目标检测、跟踪及行为识别任务。我们采用经多数据集配置（将PMOF与现有顶置鱼眼数据集结合）微调的YOLO26m-obb模型对PMOF进行基准测试。结合自定义旋转感知增强的跨域微调在PMOF上达到94.8%的AP50，并在来自不同领域的未见顶置鱼眼数据集上达到96.5%的AP50。实验结果揭示了静态与运动环境间的域差距，表明引入PMOF可提升检测性能，并将泛化能力从乘客监测任务拓展至更广泛的基于鱼眼图像的人物检测任务。数据集与代码开源于https://swermuth.github.io/pmof/。